Overview

Dataset statistics

Number of variables101
Number of observations23061
Missing cells1840621
Missing cells (%)79.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.4 MiB
Average record size in memory838.9 B

Variable types

Unsupported9
Categorical36
Boolean45
Numeric11

Alerts

ball_receipt_outcome has constant value "Incomplete"Constant
ball_recovery_recovery_failure has constant value "True"Constant
block_deflection has constant value "True"Constant
block_offensive has constant value "True"Constant
clearance_aerial_won has constant value "True"Constant
clearance_head has constant value "True"Constant
clearance_left_foot has constant value "True"Constant
clearance_right_foot has constant value "True"Constant
counterpress has constant value "True"Constant
dribble_nutmeg has constant value "True"Constant
dribble_overrun has constant value "True"Constant
foul_committed_advantage has constant value "True"Constant
foul_won_advantage has constant value "True"Constant
foul_won_defensive has constant value "True"Constant
half_start_late_video_start has constant value "True"Constant
off_camera has constant value "True"Constant
out has constant value "True"Constant
pass_aerial_won has constant value "True"Constant
pass_cross has constant value "True"Constant
pass_cut_back has constant value "True"Constant
pass_goal_assist has constant value "True"Constant
pass_inswinging has constant value "True"Constant
pass_outswinging has constant value "True"Constant
pass_shot_assist has constant value "True"Constant
pass_straight has constant value "True"Constant
pass_switch has constant value "True"Constant
pass_through_ball has constant value "True"Constant
shot_aerial_won has constant value "True"Constant
shot_deflected has constant value "True"Constant
shot_first_time has constant value "True"Constant
shot_one_on_one has constant value "True"Constant
shot_open_goal has constant value "True"Constant
under_pressure has constant value "True"Constant
goalkeeper_lost_in_play has constant value "True"Constant
goalkeeper_punched_out has constant value "True"Constant
foul_committed_offensive has constant value "True"Constant
foul_committed_penalty has constant value "True"Constant
foul_won_penalty has constant value "True"Constant
miscontrol_aerial_won has constant value "True"Constant
pass_miscommunication has constant value "True"Constant
pass_no_touch has constant value "True"Constant
injury_stoppage_in_chain has constant value "True"Constant
clearance_other has constant value "True"Constant
goalkeeper_shot_saved_off_target has constant value "True"Constant
shot_saved_off_target has constant value "True"Constant
pass_deflected has constant value "True"Constant
id has a high cardinality: 23061 distinct valuesHigh cardinality
pass_assisted_shot_id has a high cardinality: 124 distinct valuesHigh cardinality
pass_recipient has a high cardinality: 117 distinct valuesHigh cardinality
player has a high cardinality: 117 distinct valuesHigh cardinality
shot_key_pass_id has a high cardinality: 124 distinct valuesHigh cardinality
timestamp has a high cardinality: 15709 distinct valuesHigh cardinality
foul_committed_card is highly imbalanced (70.3%)Imbalance
goalkeeper_position is highly imbalanced (75.6%)Imbalance
pass_body_part is highly imbalanced (56.4%)Imbalance
pass_outcome is highly imbalanced (62.8%)Imbalance
shot_technique is highly imbalanced (61.4%)Imbalance
shot_type is highly imbalanced (82.7%)Imbalance
substitution_outcome is highly imbalanced (71.4%)Imbalance
50_50 has 23051 (> 99.9%) missing valuesMissing
ball_receipt_outcome has 21960 (95.2%) missing valuesMissing
ball_recovery_recovery_failure has 23002 (99.7%) missing valuesMissing
block_deflection has 23056 (> 99.9%) missing valuesMissing
block_offensive has 23056 (> 99.9%) missing valuesMissing
carry_end_location has 17945 (77.8%) missing valuesMissing
clearance_aerial_won has 22988 (99.7%) missing valuesMissing
clearance_body_part has 22685 (98.4%) missing valuesMissing
clearance_head has 22865 (99.2%) missing valuesMissing
clearance_left_foot has 22992 (99.7%) missing valuesMissing
clearance_right_foot has 22953 (99.5%) missing valuesMissing
counterpress has 22295 (96.7%) missing valuesMissing
dribble_nutmeg has 23051 (> 99.9%) missing valuesMissing
dribble_outcome has 22759 (98.7%) missing valuesMissing
dribble_overrun has 23046 (99.9%) missing valuesMissing
duel_outcome has 22747 (98.6%) missing valuesMissing
duel_type has 22493 (97.5%) missing valuesMissing
duration has 5722 (24.8%) missing valuesMissing
foul_committed_advantage has 23050 (> 99.9%) missing valuesMissing
foul_committed_card has 23042 (99.9%) missing valuesMissing
foul_committed_type has 23048 (99.9%) missing valuesMissing
foul_won_advantage has 23048 (99.9%) missing valuesMissing
foul_won_defensive has 23019 (99.8%) missing valuesMissing
goalkeeper_body_part has 23008 (99.8%) missing valuesMissing
goalkeeper_end_location has 22957 (99.5%) missing valuesMissing
goalkeeper_outcome has 22949 (99.5%) missing valuesMissing
goalkeeper_position has 22883 (99.2%) missing valuesMissing
goalkeeper_technique has 22987 (99.7%) missing valuesMissing
goalkeeper_type has 22844 (99.1%) missing valuesMissing
half_start_late_video_start has 23057 (> 99.9%) missing valuesMissing
interception_outcome has 22932 (99.4%) missing valuesMissing
off_camera has 22864 (99.1%) missing valuesMissing
out has 22840 (99.0%) missing valuesMissing
pass_aerial_won has 22905 (99.3%) missing valuesMissing
pass_angle has 16828 (73.0%) missing valuesMissing
pass_assisted_shot_id has 22937 (99.5%) missing valuesMissing
pass_body_part has 17371 (75.3%) missing valuesMissing
pass_cross has 22910 (99.3%) missing valuesMissing
pass_cut_back has 23045 (99.9%) missing valuesMissing
pass_end_location has 16828 (73.0%) missing valuesMissing
pass_goal_assist has 23043 (99.9%) missing valuesMissing
pass_height has 16828 (73.0%) missing valuesMissing
pass_inswinging has 23025 (99.8%) missing valuesMissing
pass_length has 16828 (73.0%) missing valuesMissing
pass_outcome has 21449 (93.0%) missing valuesMissing
pass_outswinging has 23053 (> 99.9%) missing valuesMissing
pass_recipient has 17339 (75.2%) missing valuesMissing
pass_shot_assist has 22955 (99.5%) missing valuesMissing
pass_straight has 23050 (> 99.9%) missing valuesMissing
pass_switch has 22910 (99.3%) missing valuesMissing
pass_technique has 22978 (99.6%) missing valuesMissing
pass_through_ball has 23033 (99.9%) missing valuesMissing
pass_type has 21608 (93.7%) missing valuesMissing
related_events has 1027 (4.5%) missing valuesMissing
shot_aerial_won has 23045 (99.9%) missing valuesMissing
shot_body_part has 22884 (99.2%) missing valuesMissing
shot_deflected has 23059 (> 99.9%) missing valuesMissing
shot_end_location has 22884 (99.2%) missing valuesMissing
shot_first_time has 23005 (99.8%) missing valuesMissing
shot_freeze_frame has 22888 (99.2%) missing valuesMissing
shot_key_pass_id has 22937 (99.5%) missing valuesMissing
shot_one_on_one has 23055 (> 99.9%) missing valuesMissing
shot_open_goal has 23060 (> 99.9%) missing valuesMissing
shot_outcome has 22884 (99.2%) missing valuesMissing
shot_statsbomb_xg has 22884 (99.2%) missing valuesMissing
shot_technique has 22884 (99.2%) missing valuesMissing
shot_type has 22884 (99.2%) missing valuesMissing
substitution_outcome has 23021 (99.8%) missing valuesMissing
substitution_replacement has 23021 (99.8%) missing valuesMissing
tactics has 23035 (99.9%) missing valuesMissing
under_pressure has 17861 (77.5%) missing valuesMissing
goalkeeper_lost_in_play has 23060 (> 99.9%) missing valuesMissing
goalkeeper_punched_out has 23060 (> 99.9%) missing valuesMissing
foul_committed_offensive has 23052 (> 99.9%) missing valuesMissing
foul_committed_penalty has 23056 (> 99.9%) missing valuesMissing
foul_won_penalty has 23056 (> 99.9%) missing valuesMissing
miscontrol_aerial_won has 23052 (> 99.9%) missing valuesMissing
pass_miscommunication has 23058 (> 99.9%) missing valuesMissing
pass_no_touch has 23059 (> 99.9%) missing valuesMissing
injury_stoppage_in_chain has 23055 (> 99.9%) missing valuesMissing
clearance_other has 23058 (> 99.9%) missing valuesMissing
goalkeeper_shot_saved_off_target has 23059 (> 99.9%) missing valuesMissing
shot_saved_off_target has 23059 (> 99.9%) missing valuesMissing
pass_deflected has 23059 (> 99.9%) missing valuesMissing
id is uniformly distributedUniform
pass_assisted_shot_id is uniformly distributedUniform
shot_key_pass_id is uniformly distributedUniform
timestamp is uniformly distributedUniform
id has unique valuesUnique
50_50 is an unsupported type, check if it needs cleaning or further analysisUnsupported
carry_end_location is an unsupported type, check if it needs cleaning or further analysisUnsupported
goalkeeper_end_location is an unsupported type, check if it needs cleaning or further analysisUnsupported
location is an unsupported type, check if it needs cleaning or further analysisUnsupported
pass_end_location is an unsupported type, check if it needs cleaning or further analysisUnsupported
related_events is an unsupported type, check if it needs cleaning or further analysisUnsupported
shot_end_location is an unsupported type, check if it needs cleaning or further analysisUnsupported
shot_freeze_frame is an unsupported type, check if it needs cleaning or further analysisUnsupported
tactics is an unsupported type, check if it needs cleaning or further analysisUnsupported
duration has 3691 (16.0%) zerosZeros
minute has 364 (1.6%) zerosZeros
second has 453 (2.0%) zerosZeros

Reproduction

Analysis started2023-02-07 23:51:15.565729
Analysis finished2023-02-07 23:52:32.886545
Duration1 minute and 17.32 seconds
Software versionpandas-profiling vv3.6.3
Download configurationconfig.json

Variables

50_50
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing23051
Missing (%)> 99.9%
Memory size360.3 KiB

ball_receipt_outcome
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing21960
Missing (%)95.2%
Memory size360.3 KiB
Incomplete
1101 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters11010
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIncomplete
2nd rowIncomplete
3rd rowIncomplete
4th rowIncomplete
5th rowIncomplete

Common Values

ValueCountFrequency (%)
Incomplete 1101
 
4.8%
(Missing) 21960
95.2%

Length

2023-02-07T18:52:32.948550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-07T18:52:33.039569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
incomplete 1101
100.0%

Most occurring characters

ValueCountFrequency (%)
e 2202
20.0%
I 1101
10.0%
n 1101
10.0%
c 1101
10.0%
o 1101
10.0%
m 1101
10.0%
p 1101
10.0%
l 1101
10.0%
t 1101
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9909
90.0%
Uppercase Letter 1101
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2202
22.2%
n 1101
11.1%
c 1101
11.1%
o 1101
11.1%
m 1101
11.1%
p 1101
11.1%
l 1101
11.1%
t 1101
11.1%
Uppercase Letter
ValueCountFrequency (%)
I 1101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11010
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2202
20.0%
I 1101
10.0%
n 1101
10.0%
c 1101
10.0%
o 1101
10.0%
m 1101
10.0%
p 1101
10.0%
l 1101
10.0%
t 1101
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11010
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2202
20.0%
I 1101
10.0%
n 1101
10.0%
c 1101
10.0%
o 1101
10.0%
m 1101
10.0%
p 1101
10.0%
l 1101
10.0%
t 1101
10.0%

ball_recovery_recovery_failure
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)1.7%
Missing23002
Missing (%)99.7%
Memory size360.3 KiB
True
 
59
(Missing)
23002 
ValueCountFrequency (%)
True 59
 
0.3%
(Missing) 23002
99.7%
2023-02-07T18:52:33.109586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

block_deflection
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)20.0%
Missing23056
Missing (%)> 99.9%
Memory size360.3 KiB
True
 
5
(Missing)
23056 
ValueCountFrequency (%)
True 5
 
< 0.1%
(Missing) 23056
> 99.9%
2023-02-07T18:52:33.180603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

block_offensive
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)20.0%
Missing23056
Missing (%)> 99.9%
Memory size360.3 KiB
True
 
5
(Missing)
23056 
ValueCountFrequency (%)
True 5
 
< 0.1%
(Missing) 23056
> 99.9%
2023-02-07T18:52:33.252619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

carry_end_location
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing17945
Missing (%)77.8%
Memory size360.3 KiB

clearance_aerial_won
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)1.4%
Missing22988
Missing (%)99.7%
Memory size360.3 KiB
True
 
73
(Missing)
22988 
ValueCountFrequency (%)
True 73
 
0.3%
(Missing) 22988
99.7%
2023-02-07T18:52:33.328635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

clearance_body_part
Categorical

Distinct4
Distinct (%)1.1%
Missing22685
Missing (%)98.4%
Memory size360.3 KiB
Head
196 
Right Foot
108 
Left Foot
69 
Other
 
3

Length

Max length10
Median length4
Mean length6.6489362
Min length4

Characters and Unicode

Total characters2500
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHead
2nd rowHead
3rd rowRight Foot
4th rowHead
5th rowLeft Foot

Common Values

ValueCountFrequency (%)
Head 196
 
0.8%
Right Foot 108
 
0.5%
Left Foot 69
 
0.3%
Other 3
 
< 0.1%
(Missing) 22685
98.4%

Length

2023-02-07T18:52:33.405652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-07T18:52:33.506686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
head 196
35.4%
foot 177
32.0%
right 108
19.5%
left 69
 
12.5%
other 3
 
0.5%

Most occurring characters

ValueCountFrequency (%)
t 357
14.3%
o 354
14.2%
e 268
10.7%
H 196
7.8%
a 196
7.8%
d 196
7.8%
177
7.1%
F 177
7.1%
h 111
 
4.4%
R 108
 
4.3%
Other values (6) 360
14.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1770
70.8%
Uppercase Letter 553
 
22.1%
Space Separator 177
 
7.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 357
20.2%
o 354
20.0%
e 268
15.1%
a 196
11.1%
d 196
11.1%
h 111
 
6.3%
i 108
 
6.1%
g 108
 
6.1%
f 69
 
3.9%
r 3
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
H 196
35.4%
F 177
32.0%
R 108
19.5%
L 69
 
12.5%
O 3
 
0.5%
Space Separator
ValueCountFrequency (%)
177
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2323
92.9%
Common 177
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 357
15.4%
o 354
15.2%
e 268
11.5%
H 196
8.4%
a 196
8.4%
d 196
8.4%
F 177
7.6%
h 111
 
4.8%
R 108
 
4.6%
i 108
 
4.6%
Other values (5) 252
10.8%
Common
ValueCountFrequency (%)
177
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 357
14.3%
o 354
14.2%
e 268
10.7%
H 196
7.8%
a 196
7.8%
d 196
7.8%
177
7.1%
F 177
7.1%
h 111
 
4.4%
R 108
 
4.3%
Other values (6) 360
14.4%

clearance_head
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.5%
Missing22865
Missing (%)99.2%
Memory size360.3 KiB
True
 
196
(Missing)
22865 
ValueCountFrequency (%)
True 196
 
0.8%
(Missing) 22865
99.2%
2023-02-07T18:52:33.599711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

clearance_left_foot
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)1.4%
Missing22992
Missing (%)99.7%
Memory size360.3 KiB
True
 
69
(Missing)
22992 
ValueCountFrequency (%)
True 69
 
0.3%
(Missing) 22992
99.7%
2023-02-07T18:52:33.674724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

clearance_right_foot
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.9%
Missing22953
Missing (%)99.5%
Memory size360.3 KiB
True
 
108
(Missing)
22953 
ValueCountFrequency (%)
True 108
 
0.5%
(Missing) 22953
99.5%
2023-02-07T18:52:33.746731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

counterpress
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing22295
Missing (%)96.7%
Memory size360.3 KiB
True
 
766
(Missing)
22295 
ValueCountFrequency (%)
True 766
 
3.3%
(Missing) 22295
96.7%
2023-02-07T18:52:33.819757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

dribble_nutmeg
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)10.0%
Missing23051
Missing (%)> 99.9%
Memory size360.3 KiB
True
 
10
(Missing)
23051 
ValueCountFrequency (%)
True 10
 
< 0.1%
(Missing) 23051
> 99.9%
2023-02-07T18:52:33.892773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

dribble_outcome
Categorical

Distinct2
Distinct (%)0.7%
Missing22759
Missing (%)98.7%
Memory size360.3 KiB
Complete
174 
Incomplete
128 

Length

Max length10
Median length8
Mean length8.8476821
Min length8

Characters and Unicode

Total characters2672
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowComplete
2nd rowComplete
3rd rowComplete
4th rowIncomplete
5th rowComplete

Common Values

ValueCountFrequency (%)
Complete 174
 
0.8%
Incomplete 128
 
0.6%
(Missing) 22759
98.7%

Length

2023-02-07T18:52:33.970791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-07T18:52:34.072814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
complete 174
57.6%
incomplete 128
42.4%

Most occurring characters

ValueCountFrequency (%)
e 604
22.6%
o 302
11.3%
m 302
11.3%
p 302
11.3%
l 302
11.3%
t 302
11.3%
C 174
 
6.5%
I 128
 
4.8%
n 128
 
4.8%
c 128
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2370
88.7%
Uppercase Letter 302
 
11.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 604
25.5%
o 302
12.7%
m 302
12.7%
p 302
12.7%
l 302
12.7%
t 302
12.7%
n 128
 
5.4%
c 128
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
C 174
57.6%
I 128
42.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 2672
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 604
22.6%
o 302
11.3%
m 302
11.3%
p 302
11.3%
l 302
11.3%
t 302
11.3%
C 174
 
6.5%
I 128
 
4.8%
n 128
 
4.8%
c 128
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 604
22.6%
o 302
11.3%
m 302
11.3%
p 302
11.3%
l 302
11.3%
t 302
11.3%
C 174
 
6.5%
I 128
 
4.8%
n 128
 
4.8%
c 128
 
4.8%

dribble_overrun
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)6.7%
Missing23046
Missing (%)99.9%
Memory size360.3 KiB
True
 
15
(Missing)
23046 
ValueCountFrequency (%)
True 15
 
0.1%
(Missing) 23046
99.9%
2023-02-07T18:52:34.155833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

duel_outcome
Categorical

Distinct5
Distinct (%)1.6%
Missing22747
Missing (%)98.6%
Memory size360.3 KiB
Success In Play
91 
Won
85 
Lost In Play
63 
Lost Out
61 
Success Out
14 

Length

Max length15
Median length12
Mean length9.611465
Min length3

Characters and Unicode

Total characters3018
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLost In Play
2nd rowLost In Play
3rd rowSuccess In Play
4th rowLost Out
5th rowSuccess In Play

Common Values

ValueCountFrequency (%)
Success In Play 91
 
0.4%
Won 85
 
0.4%
Lost In Play 63
 
0.3%
Lost Out 61
 
0.3%
Success Out 14
 
0.1%
(Missing) 22747
98.6%

Length

2023-02-07T18:52:34.232854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-07T18:52:34.334862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
in 154
22.1%
play 154
22.1%
lost 124
17.8%
success 105
15.1%
won 85
12.2%
out 75
10.8%

Most occurring characters

ValueCountFrequency (%)
383
12.7%
s 334
11.1%
n 239
 
7.9%
c 210
 
7.0%
o 209
 
6.9%
t 199
 
6.6%
u 180
 
6.0%
P 154
 
5.1%
I 154
 
5.1%
l 154
 
5.1%
Other values (7) 802
26.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1938
64.2%
Uppercase Letter 697
 
23.1%
Space Separator 383
 
12.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 334
17.2%
n 239
12.3%
c 210
10.8%
o 209
10.8%
t 199
10.3%
u 180
9.3%
l 154
7.9%
a 154
7.9%
y 154
7.9%
e 105
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
P 154
22.1%
I 154
22.1%
L 124
17.8%
S 105
15.1%
W 85
12.2%
O 75
10.8%
Space Separator
ValueCountFrequency (%)
383
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2635
87.3%
Common 383
 
12.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 334
12.7%
n 239
 
9.1%
c 210
 
8.0%
o 209
 
7.9%
t 199
 
7.6%
u 180
 
6.8%
P 154
 
5.8%
I 154
 
5.8%
l 154
 
5.8%
a 154
 
5.8%
Other values (6) 648
24.6%
Common
ValueCountFrequency (%)
383
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3018
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
383
12.7%
s 334
11.1%
n 239
 
7.9%
c 210
 
7.0%
o 209
 
6.9%
t 199
 
6.6%
u 180
 
6.0%
P 154
 
5.1%
I 154
 
5.1%
l 154
 
5.1%
Other values (7) 802
26.6%

duel_type
Categorical

Distinct2
Distinct (%)0.4%
Missing22493
Missing (%)97.5%
Memory size360.3 KiB
Tackle
314 
Aerial Lost
254 

Length

Max length11
Median length6
Mean length8.2359155
Min length6

Characters and Unicode

Total characters4678
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAerial Lost
2nd rowAerial Lost
3rd rowAerial Lost
4th rowTackle
5th rowAerial Lost

Common Values

ValueCountFrequency (%)
Tackle 314
 
1.4%
Aerial Lost 254
 
1.1%
(Missing) 22493
97.5%

Length

2023-02-07T18:52:34.436885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-07T18:52:34.537909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
tackle 314
38.2%
aerial 254
30.9%
lost 254
30.9%

Most occurring characters

ValueCountFrequency (%)
a 568
12.1%
l 568
12.1%
e 568
12.1%
T 314
 
6.7%
c 314
 
6.7%
k 314
 
6.7%
A 254
 
5.4%
r 254
 
5.4%
i 254
 
5.4%
254
 
5.4%
Other values (4) 1016
21.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3602
77.0%
Uppercase Letter 822
 
17.6%
Space Separator 254
 
5.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 568
15.8%
l 568
15.8%
e 568
15.8%
c 314
8.7%
k 314
8.7%
r 254
7.1%
i 254
7.1%
o 254
7.1%
s 254
7.1%
t 254
7.1%
Uppercase Letter
ValueCountFrequency (%)
T 314
38.2%
A 254
30.9%
L 254
30.9%
Space Separator
ValueCountFrequency (%)
254
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4424
94.6%
Common 254
 
5.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 568
12.8%
l 568
12.8%
e 568
12.8%
T 314
7.1%
c 314
7.1%
k 314
7.1%
A 254
 
5.7%
r 254
 
5.7%
i 254
 
5.7%
L 254
 
5.7%
Other values (3) 762
17.2%
Common
ValueCountFrequency (%)
254
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 568
12.1%
l 568
12.1%
e 568
12.1%
T 314
 
6.7%
c 314
 
6.7%
k 314
 
6.7%
A 254
 
5.4%
r 254
 
5.4%
i 254
 
5.4%
254
 
5.4%
Other values (4) 1016
21.7%

duration
Real number (ℝ)

MISSING  ZEROS 

Distinct12929
Distinct (%)74.6%
Missing5722
Missing (%)24.8%
Infinite0
Infinite (%)0.0%
Mean1.2413595
Minimum0
Maximum26.237043
Zeros3691
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size360.3 KiB
2023-02-07T18:52:34.630940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.12
median1.035386
Q31.741169
95-th percentile3.4656133
Maximum26.237043
Range26.237043
Interquartile range (IQR)1.621169

Descriptive statistics

Standard deviation1.3539269
Coefficient of variation (CV)1.0906808
Kurtosis28.975339
Mean1.2413595
Median Absolute Deviation (MAD)0.779346
Skewness3.4507287
Sum21523.932
Variance1.8331182
MonotonicityNot monotonic
2023-02-07T18:52:34.753968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3691
 
16.0%
0.04 94
 
0.4%
0.08 83
 
0.4%
0.040000003 68
 
0.3%
0.12 17
 
0.1%
0.16 5
 
< 0.1%
0.1 5
 
< 0.1%
0.039957 4
 
< 0.1%
0.039999 4
 
< 0.1%
0.039962 4
 
< 0.1%
Other values (12919) 13364
58.0%
(Missing) 5722
24.8%
ValueCountFrequency (%)
0 3691
16.0%
1.3 × 10-51
 
< 0.1%
0.0017 1
 
< 0.1%
0.003001 1
 
< 0.1%
0.003893 1
 
< 0.1%
0.004238 1
 
< 0.1%
0.005635 1
 
< 0.1%
0.005688 1
 
< 0.1%
0.0067 1
 
< 0.1%
0.008292001 1
 
< 0.1%
ValueCountFrequency (%)
26.237043 1
< 0.1%
21.643747 1
< 0.1%
19.32515 1
< 0.1%
18.1814 1
< 0.1%
17.8916 1
< 0.1%
16.89646 1
< 0.1%
16.763248 1
< 0.1%
16.6629 1
< 0.1%
16.2853 1
< 0.1%
16.0921 1
< 0.1%

foul_committed_advantage
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)9.1%
Missing23050
Missing (%)> 99.9%
Memory size360.3 KiB
True
 
11
(Missing)
23050 
ValueCountFrequency (%)
True 11
 
< 0.1%
(Missing) 23050
> 99.9%
2023-02-07T18:52:35.183054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

foul_committed_card
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)10.5%
Missing23042
Missing (%)99.9%
Memory size360.3 KiB
Yellow Card
18 
Second Yellow
 
1

Length

Max length13
Median length11
Mean length11.105263
Min length11

Characters and Unicode

Total characters211
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)5.3%

Sample

1st rowYellow Card
2nd rowYellow Card
3rd rowYellow Card
4th rowYellow Card
5th rowYellow Card

Common Values

ValueCountFrequency (%)
Yellow Card 18
 
0.1%
Second Yellow 1
 
< 0.1%
(Missing) 23042
99.9%

Length

2023-02-07T18:52:35.262072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-07T18:52:35.367095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
yellow 19
50.0%
card 18
47.4%
second 1
 
2.6%

Most occurring characters

ValueCountFrequency (%)
l 38
18.0%
e 20
9.5%
o 20
9.5%
Y 19
9.0%
w 19
9.0%
19
9.0%
d 19
9.0%
C 18
8.5%
a 18
8.5%
r 18
8.5%
Other values (3) 3
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 154
73.0%
Uppercase Letter 38
 
18.0%
Space Separator 19
 
9.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 38
24.7%
e 20
13.0%
o 20
13.0%
w 19
12.3%
d 19
12.3%
a 18
11.7%
r 18
11.7%
c 1
 
0.6%
n 1
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
Y 19
50.0%
C 18
47.4%
S 1
 
2.6%
Space Separator
ValueCountFrequency (%)
19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 192
91.0%
Common 19
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 38
19.8%
e 20
10.4%
o 20
10.4%
Y 19
9.9%
w 19
9.9%
d 19
9.9%
C 18
9.4%
a 18
9.4%
r 18
9.4%
S 1
 
0.5%
Other values (2) 2
 
1.0%
Common
ValueCountFrequency (%)
19
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 211
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 38
18.0%
e 20
9.5%
o 20
9.5%
Y 19
9.0%
w 19
9.0%
19
9.0%
d 19
9.0%
C 18
8.5%
a 18
8.5%
r 18
8.5%
Other values (3) 3
 
1.4%

foul_committed_type
Categorical

Distinct4
Distinct (%)30.8%
Missing23048
Missing (%)99.9%
Memory size360.3 KiB
Handball
8 
Foul Out
3 
Dangerous Play
1 
6 Seconds
1 

Length

Max length14
Median length8
Mean length8.5384615
Min length8

Characters and Unicode

Total characters111
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)15.4%

Sample

1st rowHandball
2nd rowHandball
3rd rowHandball
4th rowHandball
5th rowFoul Out

Common Values

ValueCountFrequency (%)
Handball 8
 
< 0.1%
Foul Out 3
 
< 0.1%
Dangerous Play 1
 
< 0.1%
6 Seconds 1
 
< 0.1%
(Missing) 23048
99.9%

Length

2023-02-07T18:52:35.452115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-07T18:52:35.553137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
handball 8
44.4%
foul 3
 
16.7%
out 3
 
16.7%
dangerous 1
 
5.6%
play 1
 
5.6%
6 1
 
5.6%
seconds 1
 
5.6%

Most occurring characters

ValueCountFrequency (%)
l 20
18.0%
a 18
16.2%
n 10
9.0%
d 9
8.1%
H 8
 
7.2%
b 8
 
7.2%
u 7
 
6.3%
o 5
 
4.5%
5
 
4.5%
O 3
 
2.7%
Other values (12) 18
16.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 88
79.3%
Uppercase Letter 17
 
15.3%
Space Separator 5
 
4.5%
Decimal Number 1
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 20
22.7%
a 18
20.5%
n 10
11.4%
d 9
10.2%
b 8
 
9.1%
u 7
 
8.0%
o 5
 
5.7%
t 3
 
3.4%
e 2
 
2.3%
s 2
 
2.3%
Other values (4) 4
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
H 8
47.1%
O 3
 
17.6%
F 3
 
17.6%
D 1
 
5.9%
P 1
 
5.9%
S 1
 
5.9%
Space Separator
ValueCountFrequency (%)
5
100.0%
Decimal Number
ValueCountFrequency (%)
6 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 105
94.6%
Common 6
 
5.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 20
19.0%
a 18
17.1%
n 10
9.5%
d 9
8.6%
H 8
 
7.6%
b 8
 
7.6%
u 7
 
6.7%
o 5
 
4.8%
O 3
 
2.9%
t 3
 
2.9%
Other values (10) 14
13.3%
Common
ValueCountFrequency (%)
5
83.3%
6 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 20
18.0%
a 18
16.2%
n 10
9.0%
d 9
8.1%
H 8
 
7.2%
b 8
 
7.2%
u 7
 
6.3%
o 5
 
4.5%
5
 
4.5%
O 3
 
2.7%
Other values (12) 18
16.2%

foul_won_advantage
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)7.7%
Missing23048
Missing (%)99.9%
Memory size360.3 KiB
True
 
13
(Missing)
23048 
ValueCountFrequency (%)
True 13
 
0.1%
(Missing) 23048
99.9%
2023-02-07T18:52:35.642157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

foul_won_defensive
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)2.4%
Missing23019
Missing (%)99.8%
Memory size360.3 KiB
True
 
42
(Missing)
23019 
ValueCountFrequency (%)
True 42
 
0.2%
(Missing) 23019
99.8%
2023-02-07T18:52:35.725178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

goalkeeper_body_part
Categorical

Distinct7
Distinct (%)13.2%
Missing23008
Missing (%)99.8%
Memory size360.3 KiB
Both Hands
34 
Left Foot
6 
Right Foot
4 
Right Hand
4 
Head
 
2
Other values (2)
 
3

Length

Max length10
Median length10
Mean length9.5283019
Min length4

Characters and Unicode

Total characters505
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.9%

Sample

1st rowBoth Hands
2nd rowBoth Hands
3rd rowBoth Hands
4th rowBoth Hands
5th rowBoth Hands

Common Values

ValueCountFrequency (%)
Both Hands 34
 
0.1%
Left Foot 6
 
< 0.1%
Right Foot 4
 
< 0.1%
Right Hand 4
 
< 0.1%
Head 2
 
< 0.1%
Left Hand 2
 
< 0.1%
Chest 1
 
< 0.1%
(Missing) 23008
99.8%

Length

2023-02-07T18:52:35.807196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-07T18:52:35.912219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
both 34
33.0%
hands 34
33.0%
foot 10
 
9.7%
left 8
 
7.8%
right 8
 
7.8%
hand 6
 
5.8%
head 2
 
1.9%
chest 1
 
1.0%

Most occurring characters

ValueCountFrequency (%)
t 61
12.1%
o 54
10.7%
50
9.9%
h 43
8.5%
H 42
8.3%
a 42
8.3%
d 42
8.3%
n 40
7.9%
s 35
6.9%
B 34
6.7%
Other values (8) 62
12.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 352
69.7%
Uppercase Letter 103
 
20.4%
Space Separator 50
 
9.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 61
17.3%
o 54
15.3%
h 43
12.2%
a 42
11.9%
d 42
11.9%
n 40
11.4%
s 35
9.9%
e 11
 
3.1%
f 8
 
2.3%
i 8
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
H 42
40.8%
B 34
33.0%
F 10
 
9.7%
L 8
 
7.8%
R 8
 
7.8%
C 1
 
1.0%
Space Separator
ValueCountFrequency (%)
50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 455
90.1%
Common 50
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 61
13.4%
o 54
11.9%
h 43
9.5%
H 42
9.2%
a 42
9.2%
d 42
9.2%
n 40
8.8%
s 35
7.7%
B 34
7.5%
e 11
 
2.4%
Other values (7) 51
11.2%
Common
ValueCountFrequency (%)
50
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 505
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 61
12.1%
o 54
10.7%
50
9.9%
h 43
8.5%
H 42
8.3%
a 42
8.3%
d 42
8.3%
n 40
7.9%
s 35
6.9%
B 34
6.7%
Other values (8) 62
12.3%

goalkeeper_end_location
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing22957
Missing (%)99.5%
Memory size360.3 KiB

goalkeeper_outcome
Categorical

Distinct14
Distinct (%)12.5%
Missing22949
Missing (%)99.5%
Memory size360.3 KiB
Success
35 
No Touch
25 
In Play Danger
15 
Touched Out
9 
In Play Safe
9 
Other values (9)
19 

Length

Max length15
Median length14
Mean length9.0714286
Min length3

Characters and Unicode

Total characters1016
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)4.5%

Sample

1st rowSaved Twice
2nd rowCollected Twice
3rd rowNo Touch
4th rowTouched In
5th rowIn Play Danger

Common Values

ValueCountFrequency (%)
Success 35
 
0.2%
No Touch 25
 
0.1%
In Play Danger 15
 
0.1%
Touched Out 9
 
< 0.1%
In Play Safe 9
 
< 0.1%
Clear 5
 
< 0.1%
Saved Twice 4
 
< 0.1%
Touched In 3
 
< 0.1%
Claim 2
 
< 0.1%
Collected Twice 1
 
< 0.1%
Other values (4) 4
 
< 0.1%
(Missing) 22949
99.5%

Length

2023-02-07T18:52:36.015243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
success 35
17.1%
in 28
13.7%
touch 25
12.2%
play 25
12.2%
no 25
12.2%
danger 15
7.3%
touched 12
 
5.9%
out 10
 
4.9%
safe 9
 
4.4%
twice 5
 
2.4%
Other values (8) 16
7.8%

Most occurring characters

ValueCountFrequency (%)
c 114
 
11.2%
93
 
9.2%
e 88
 
8.7%
u 83
 
8.2%
s 71
 
7.0%
o 66
 
6.5%
a 61
 
6.0%
S 48
 
4.7%
n 45
 
4.4%
T 42
 
4.1%
Other values (21) 305
30.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 719
70.8%
Uppercase Letter 204
 
20.1%
Space Separator 93
 
9.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 114
15.9%
e 88
12.2%
u 83
11.5%
s 71
9.9%
o 66
9.2%
a 61
8.5%
n 45
 
6.3%
h 38
 
5.3%
l 35
 
4.9%
y 25
 
3.5%
Other values (9) 93
12.9%
Uppercase Letter
ValueCountFrequency (%)
S 48
23.5%
T 42
20.6%
I 28
13.7%
P 26
12.7%
N 25
12.3%
D 15
 
7.4%
O 9
 
4.4%
C 8
 
3.9%
F 1
 
0.5%
L 1
 
0.5%
Space Separator
ValueCountFrequency (%)
93
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 923
90.8%
Common 93
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 114
12.4%
e 88
 
9.5%
u 83
 
9.0%
s 71
 
7.7%
o 66
 
7.2%
a 61
 
6.6%
S 48
 
5.2%
n 45
 
4.9%
T 42
 
4.6%
h 38
 
4.1%
Other values (20) 267
28.9%
Common
ValueCountFrequency (%)
93
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1016
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 114
 
11.2%
93
 
9.2%
e 88
 
8.7%
u 83
 
8.2%
s 71
 
7.0%
o 66
 
6.5%
a 61
 
6.0%
S 48
 
4.7%
n 45
 
4.4%
T 42
 
4.1%
Other values (21) 305
30.0%

goalkeeper_position
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)1.7%
Missing22883
Missing (%)99.2%
Memory size360.3 KiB
Set
167 
Moving
 
8
Prone
 
3

Length

Max length6
Median length3
Mean length3.1685393
Min length3

Characters and Unicode

Total characters564
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSet
2nd rowSet
3rd rowSet
4th rowSet
5th rowMoving

Common Values

ValueCountFrequency (%)
Set 167
 
0.7%
Moving 8
 
< 0.1%
Prone 3
 
< 0.1%
(Missing) 22883
99.2%

Length

2023-02-07T18:52:36.116264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-07T18:52:36.222289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
set 167
93.8%
moving 8
 
4.5%
prone 3
 
1.7%

Most occurring characters

ValueCountFrequency (%)
e 170
30.1%
S 167
29.6%
t 167
29.6%
o 11
 
2.0%
n 11
 
2.0%
M 8
 
1.4%
v 8
 
1.4%
i 8
 
1.4%
g 8
 
1.4%
P 3
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 386
68.4%
Uppercase Letter 178
31.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 170
44.0%
t 167
43.3%
o 11
 
2.8%
n 11
 
2.8%
v 8
 
2.1%
i 8
 
2.1%
g 8
 
2.1%
r 3
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
S 167
93.8%
M 8
 
4.5%
P 3
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 564
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 170
30.1%
S 167
29.6%
t 167
29.6%
o 11
 
2.0%
n 11
 
2.0%
M 8
 
1.4%
v 8
 
1.4%
i 8
 
1.4%
g 8
 
1.4%
P 3
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 170
30.1%
S 167
29.6%
t 167
29.6%
o 11
 
2.0%
n 11
 
2.0%
M 8
 
1.4%
v 8
 
1.4%
i 8
 
1.4%
g 8
 
1.4%
P 3
 
0.5%

goalkeeper_technique
Categorical

Distinct2
Distinct (%)2.7%
Missing22987
Missing (%)99.7%
Memory size360.3 KiB
Standing
48 
Diving
26 

Length

Max length8
Median length8
Mean length7.2972973
Min length6

Characters and Unicode

Total characters540
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStanding
2nd rowStanding
3rd rowDiving
4th rowStanding
5th rowStanding

Common Values

ValueCountFrequency (%)
Standing 48
 
0.2%
Diving 26
 
0.1%
(Missing) 22987
99.7%

Length

2023-02-07T18:52:36.313310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-07T18:52:36.413332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
standing 48
64.9%
diving 26
35.1%

Most occurring characters

ValueCountFrequency (%)
n 122
22.6%
i 100
18.5%
g 74
13.7%
S 48
 
8.9%
t 48
 
8.9%
a 48
 
8.9%
d 48
 
8.9%
D 26
 
4.8%
v 26
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 466
86.3%
Uppercase Letter 74
 
13.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 122
26.2%
i 100
21.5%
g 74
15.9%
t 48
 
10.3%
a 48
 
10.3%
d 48
 
10.3%
v 26
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
S 48
64.9%
D 26
35.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 540
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 122
22.6%
i 100
18.5%
g 74
13.7%
S 48
 
8.9%
t 48
 
8.9%
a 48
 
8.9%
d 48
 
8.9%
D 26
 
4.8%
v 26
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 540
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 122
22.6%
i 100
18.5%
g 74
13.7%
S 48
 
8.9%
t 48
 
8.9%
a 48
 
8.9%
d 48
 
8.9%
D 26
 
4.8%
v 26
 
4.8%

goalkeeper_type
Categorical

Distinct11
Distinct (%)5.1%
Missing22844
Missing (%)99.1%
Memory size360.3 KiB
Shot Faced
105 
Shot Saved
41 
Goal Conceded
25 
Collected
18 
Punch
12 
Other values (6)
16 

Length

Max length21
Median length10
Mean length10.258065
Min length4

Characters and Unicode

Total characters2226
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.9%

Sample

1st rowShot Faced
2nd rowShot Saved
3rd rowShot Faced
4th rowCollected
5th rowShot Faced

Common Values

ValueCountFrequency (%)
Shot Faced 105
 
0.5%
Shot Saved 41
 
0.2%
Goal Conceded 25
 
0.1%
Collected 18
 
0.1%
Punch 12
 
0.1%
Keeper Sweeper 7
 
< 0.1%
Penalty Conceded 3
 
< 0.1%
Smother 2
 
< 0.1%
Shot Saved Off Target 2
 
< 0.1%
Save 1
 
< 0.1%
(Missing) 22844
99.1%

Length

2023-02-07T18:52:36.503352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
shot 148
36.5%
faced 105
25.9%
saved 44
 
10.9%
conceded 28
 
6.9%
goal 25
 
6.2%
collected 18
 
4.4%
punch 12
 
3.0%
keeper 7
 
1.7%
sweeper 7
 
1.7%
penalty 4
 
1.0%
Other values (4) 7
 
1.7%

Most occurring characters

ValueCountFrequency (%)
e 292
13.1%
d 223
10.0%
o 221
9.9%
S 202
9.1%
188
8.4%
a 181
8.1%
t 174
7.8%
c 163
7.3%
h 162
7.3%
F 105
 
4.7%
Other values (17) 315
14.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1633
73.4%
Uppercase Letter 405
 
18.2%
Space Separator 188
 
8.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 292
17.9%
d 223
13.7%
o 221
13.5%
a 181
11.1%
t 174
10.7%
c 163
10.0%
h 162
9.9%
l 65
 
4.0%
v 45
 
2.8%
n 44
 
2.7%
Other values (8) 63
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
S 202
49.9%
F 105
25.9%
C 46
 
11.4%
G 25
 
6.2%
P 16
 
4.0%
K 7
 
1.7%
O 2
 
0.5%
T 2
 
0.5%
Space Separator
ValueCountFrequency (%)
188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2038
91.6%
Common 188
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 292
14.3%
d 223
10.9%
o 221
10.8%
S 202
9.9%
a 181
8.9%
t 174
8.5%
c 163
8.0%
h 162
7.9%
F 105
 
5.2%
l 65
 
3.2%
Other values (16) 250
12.3%
Common
ValueCountFrequency (%)
188
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2226
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 292
13.1%
d 223
10.0%
o 221
9.9%
S 202
9.1%
188
8.4%
a 181
8.1%
t 174
7.8%
c 163
7.3%
h 162
7.3%
F 105
 
4.7%
Other values (17) 315
14.2%

half_start_late_video_start
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)25.0%
Missing23057
Missing (%)> 99.9%
Memory size360.3 KiB
True
 
4
(Missing)
23057 
ValueCountFrequency (%)
True 4
 
< 0.1%
(Missing) 23057
> 99.9%
2023-02-07T18:52:36.604375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

id
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct23061
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size360.3 KiB
fc118565-8701-4fa9-8e80-959385506cb3
 
1
ea124bcb-6aa6-426e-9fac-39bd3e635711
 
1
1c2a58e3-e59d-44fd-be23-f807d1f41b1c
 
1
6d4cd93a-3c24-484c-b979-0c8db0d5224e
 
1
f67f9d00-ea09-459b-be1e-d24a37f4c5d1
 
1
Other values (23056)
23056 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters830196
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23061 ?
Unique (%)100.0%

Sample

1st rowfc118565-8701-4fa9-8e80-959385506cb3
2nd row28201a35-24d6-4b28-81cd-9f0b5327b859
3rd row8647efc3-43f9-466a-8813-ddede6a37b11
4th row17f72522-5ec1-45d2-aeec-c7de0f2f4eb7
5th rowd3600eae-bcdf-41ca-b1ad-976a28ddde40

Common Values

ValueCountFrequency (%)
fc118565-8701-4fa9-8e80-959385506cb3 1
 
< 0.1%
ea124bcb-6aa6-426e-9fac-39bd3e635711 1
 
< 0.1%
1c2a58e3-e59d-44fd-be23-f807d1f41b1c 1
 
< 0.1%
6d4cd93a-3c24-484c-b979-0c8db0d5224e 1
 
< 0.1%
f67f9d00-ea09-459b-be1e-d24a37f4c5d1 1
 
< 0.1%
4a3c54dc-0471-464a-b6f4-4bbb763f324b 1
 
< 0.1%
e9c840d4-becb-40ee-a4a6-9e4d248819fe 1
 
< 0.1%
f6df9f49-c6bc-4e6c-b55c-bc92c567002a 1
 
< 0.1%
5b6a3fb1-c88a-4e3a-b29c-ca22f082e21a 1
 
< 0.1%
7961de81-77a1-40fa-b4d8-df55e44c1a51 1
 
< 0.1%
Other values (23051) 23051
> 99.9%

Length

2023-02-07T18:52:36.679403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fc118565-8701-4fa9-8e80-959385506cb3 1
 
< 0.1%
ddf01140-7770-4ff5-b052-548e9904fdfc 1
 
< 0.1%
17f72522-5ec1-45d2-aeec-c7de0f2f4eb7 1
 
< 0.1%
d3600eae-bcdf-41ca-b1ad-976a28ddde40 1
 
< 0.1%
3430db8d-2e2d-47bc-b4a0-3f5f467def9c 1
 
< 0.1%
8ffbcc19-0252-43f1-90a8-f8f65d348ebb 1
 
< 0.1%
9a8e87e0-0e0f-4629-b539-aaaccf6ed46e 1
 
< 0.1%
cfebe786-7e37-4a95-901f-31087b1be10f 1
 
< 0.1%
3062e6df-5f51-4ae3-bec5-c43eea65588d 1
 
< 0.1%
b7c30ef0-9785-4336-9d7b-42d37bc0de4d 1
 
< 0.1%
Other values (23051) 23051
> 99.9%

Most occurring characters

ValueCountFrequency (%)
- 92244
 
11.1%
4 66299
 
8.0%
b 49373
 
5.9%
a 49234
 
5.9%
9 49219
 
5.9%
8 48862
 
5.9%
5 43402
 
5.2%
e 43318
 
5.2%
c 43312
 
5.2%
3 43286
 
5.2%
Other values (7) 301647
36.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 466448
56.2%
Lowercase Letter 271504
32.7%
Dash Punctuation 92244
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 66299
14.2%
9 49219
10.6%
8 48862
10.5%
5 43402
9.3%
3 43286
9.3%
1 43231
9.3%
2 43153
9.3%
7 43151
9.3%
6 43123
9.2%
0 42722
9.2%
Lowercase Letter
ValueCountFrequency (%)
b 49373
18.2%
a 49234
18.1%
e 43318
16.0%
c 43312
16.0%
d 43196
15.9%
f 43071
15.9%
Dash Punctuation
ValueCountFrequency (%)
- 92244
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 558692
67.3%
Latin 271504
32.7%

Most frequent character per script

Common
ValueCountFrequency (%)
- 92244
16.5%
4 66299
11.9%
9 49219
8.8%
8 48862
8.7%
5 43402
7.8%
3 43286
7.7%
1 43231
7.7%
2 43153
7.7%
7 43151
7.7%
6 43123
7.7%
Latin
ValueCountFrequency (%)
b 49373
18.2%
a 49234
18.1%
e 43318
16.0%
c 43312
16.0%
d 43196
15.9%
f 43071
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 830196
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 92244
 
11.1%
4 66299
 
8.0%
b 49373
 
5.9%
a 49234
 
5.9%
9 49219
 
5.9%
8 48862
 
5.9%
5 43402
 
5.2%
e 43318
 
5.2%
c 43312
 
5.2%
3 43286
 
5.2%
Other values (7) 301647
36.3%

df_index
Real number (ℝ)

Distinct3810
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1657.7285
Minimum1
Maximum3810
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.3 KiB
2023-02-07T18:52:36.788418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile165
Q1824
median1648
Q32471
95-th percentile3168
Maximum3810
Range3809
Interquartile range (IQR)1647

Descriptive statistics

Standard deviation969.0044
Coefficient of variation (CV)0.58453744
Kurtosis-1.0938914
Mean1657.7285
Median Absolute Deviation (MAD)824
Skewness0.068224673
Sum38228878
Variance938969.53
MonotonicityNot monotonic
2023-02-07T18:52:36.912444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7
 
< 0.1%
1663 7
 
< 0.1%
1672 7
 
< 0.1%
1678 7
 
< 0.1%
1695 7
 
< 0.1%
1698 7
 
< 0.1%
1701 7
 
< 0.1%
1704 7
 
< 0.1%
1711 7
 
< 0.1%
1719 7
 
< 0.1%
Other values (3800) 22991
99.7%
ValueCountFrequency (%)
1 7
< 0.1%
2 7
< 0.1%
3 7
< 0.1%
4 7
< 0.1%
5 7
< 0.1%
6 7
< 0.1%
7 7
< 0.1%
8 7
< 0.1%
9 7
< 0.1%
10 7
< 0.1%
ValueCountFrequency (%)
3810 1
< 0.1%
3809 1
< 0.1%
3808 1
< 0.1%
3807 1
< 0.1%
3806 1
< 0.1%
3805 1
< 0.1%
3804 1
< 0.1%
3803 1
< 0.1%
3802 1
< 0.1%
3801 1
< 0.1%

interception_outcome
Categorical

Distinct5
Distinct (%)3.9%
Missing22932
Missing (%)99.4%
Memory size360.3 KiB
Won
57 
Lost Out
25 
Lost In Play
23 
Success In Play
23 
Success Out
 
1

Length

Max length15
Median length12
Mean length7.7751938
Min length3

Characters and Unicode

Total characters1003
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st rowLost In Play
2nd rowLost In Play
3rd rowWon
4th rowLost In Play
5th rowLost Out

Common Values

ValueCountFrequency (%)
Won 57
 
0.2%
Lost Out 25
 
0.1%
Lost In Play 23
 
0.1%
Success In Play 23
 
0.1%
Success Out 1
 
< 0.1%
(Missing) 22932
99.4%

Length

2023-02-07T18:52:37.033480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-07T18:52:37.136506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
won 57
23.1%
lost 48
19.4%
in 46
18.6%
play 46
18.6%
out 26
10.5%
success 24
9.7%

Most occurring characters

ValueCountFrequency (%)
118
11.8%
o 105
10.5%
n 103
 
10.3%
s 96
 
9.6%
t 74
 
7.4%
W 57
 
5.7%
u 50
 
5.0%
L 48
 
4.8%
c 48
 
4.8%
I 46
 
4.6%
Other values (7) 258
25.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 638
63.6%
Uppercase Letter 247
 
24.6%
Space Separator 118
 
11.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 105
16.5%
n 103
16.1%
s 96
15.0%
t 74
11.6%
u 50
7.8%
c 48
7.5%
l 46
7.2%
a 46
7.2%
y 46
7.2%
e 24
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
W 57
23.1%
L 48
19.4%
I 46
18.6%
P 46
18.6%
O 26
10.5%
S 24
9.7%
Space Separator
ValueCountFrequency (%)
118
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 885
88.2%
Common 118
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 105
11.9%
n 103
11.6%
s 96
10.8%
t 74
 
8.4%
W 57
 
6.4%
u 50
 
5.6%
L 48
 
5.4%
c 48
 
5.4%
I 46
 
5.2%
P 46
 
5.2%
Other values (6) 212
24.0%
Common
ValueCountFrequency (%)
118
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1003
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
118
11.8%
o 105
10.5%
n 103
 
10.3%
s 96
 
9.6%
t 74
 
7.4%
W 57
 
5.7%
u 50
 
5.0%
L 48
 
4.8%
c 48
 
4.8%
I 46
 
4.6%
Other values (7) 258
25.7%

location
Unsupported

REJECTED  UNSUPPORTED 

Missing164
Missing (%)0.7%
Memory size360.3 KiB

match_id
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55348.241
Minimum22943
Maximum69321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.3 KiB
2023-02-07T18:52:37.220529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum22943
5-th percentile22943
Q122974
median69161
Q369258
95-th percentile69321
Maximum69321
Range46378
Interquartile range (IQR)46284

Descriptive statistics

Standard deviation21051.128
Coefficient of variation (CV)0.3803396
Kurtosis-1.2099207
Mean55348.241
Median Absolute Deviation (MAD)160
Skewness-0.88831377
Sum1.2763858 × 109
Variance4.4314999 × 108
MonotonicityNot monotonic
2023-02-07T18:52:37.299543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
68345 3810
16.5%
22943 3478
15.1%
22974 3369
14.6%
69202 3163
13.7%
69258 3116
13.5%
69321 3070
13.3%
69161 3055
13.2%
ValueCountFrequency (%)
22943 3478
15.1%
22974 3369
14.6%
68345 3810
16.5%
69161 3055
13.2%
69202 3163
13.7%
69258 3116
13.5%
69321 3070
13.3%
ValueCountFrequency (%)
69321 3070
13.3%
69258 3116
13.5%
69202 3163
13.7%
69161 3055
13.2%
68345 3810
16.5%
22974 3369
14.6%
22943 3478
15.1%

minute
Real number (ℝ)

Distinct99
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.883396
Minimum0
Maximum98
Zeros364
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size360.3 KiB
2023-02-07T18:52:37.405567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q122
median46
Q368
95-th percentile90
Maximum98
Range98
Interquartile range (IQR)46

Descriptive statistics

Standard deviation27.4078
Coefficient of variation (CV)0.5973359
Kurtosis-1.1249607
Mean45.883396
Median Absolute Deviation (MAD)23
Skewness0.086608382
Sum1058117
Variance751.18749
MonotonicityNot monotonic
2023-02-07T18:52:37.524593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 515
 
2.2%
46 515
 
2.2%
47 391
 
1.7%
33 391
 
1.7%
0 364
 
1.6%
48 323
 
1.4%
12 321
 
1.4%
60 320
 
1.4%
57 310
 
1.3%
89 308
 
1.3%
Other values (89) 19303
83.7%
ValueCountFrequency (%)
0 364
1.6%
1 231
1.0%
2 251
1.1%
3 230
1.0%
4 192
0.8%
5 204
0.9%
6 276
1.2%
7 276
1.2%
8 282
1.2%
9 244
1.1%
ValueCountFrequency (%)
98 13
 
0.1%
97 51
 
0.2%
96 76
 
0.3%
95 94
 
0.4%
94 225
1.0%
93 201
0.9%
92 259
1.1%
91 198
0.9%
90 288
1.2%
89 308
1.3%

off_camera
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.5%
Missing22864
Missing (%)99.1%
Memory size360.3 KiB
True
 
197
(Missing)
22864 
ValueCountFrequency (%)
True 197
 
0.9%
(Missing) 22864
99.1%
2023-02-07T18:52:37.631607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

out
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.5%
Missing22840
Missing (%)99.0%
Memory size360.3 KiB
True
 
221
(Missing)
22840 
ValueCountFrequency (%)
True 221
 
1.0%
(Missing) 22840
99.0%
2023-02-07T18:52:37.710635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_aerial_won
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.6%
Missing22905
Missing (%)99.3%
Memory size360.3 KiB
True
 
156
(Missing)
22905 
ValueCountFrequency (%)
True 156
 
0.7%
(Missing) 22905
99.3%
2023-02-07T18:52:37.787657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_angle
Real number (ℝ)

Distinct5817
Distinct (%)93.3%
Missing16828
Missing (%)73.0%
Infinite0
Infinite (%)0.0%
Mean0.03173786
Minimum-3.1333961
Maximum3.1415927
Zeros27
Zeros (%)0.1%
Negative3075
Negative (%)13.3%
Memory size360.3 KiB
2023-02-07T18:52:37.875673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-3.1333961
5-th percentile-2.4896308
Q1-1.0443176
median0.010752274
Q31.1388384
95-th percentile2.6185653
Maximum3.1415927
Range6.2749888
Interquartile range (IQR)2.183156

Descriptive statistics

Standard deviation1.5041972
Coefficient of variation (CV)47.394411
Kurtosis-0.69704455
Mean0.03173786
Median Absolute Deviation (MAD)1.0919702
Skewness0.027113856
Sum197.82208
Variance2.2626091
MonotonicityNot monotonic
2023-02-07T18:52:37.989699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27
 
0.1%
1.5707964 26
 
0.1%
-1.5707964 22
 
0.1%
3.1415927 15
 
0.1%
-0.7853982 10
 
< 0.1%
0.7853982 8
 
< 0.1%
2.3561945 8
 
< 0.1%
-0.5880026 7
 
< 0.1%
0.4636476 7
 
< 0.1%
-2.3561945 5
 
< 0.1%
Other values (5807) 6098
 
26.4%
(Missing) 16828
73.0%
ValueCountFrequency (%)
-3.1333961 1
< 0.1%
-3.1311765 1
< 0.1%
-3.1235766 1
< 0.1%
-3.1158473 1
< 0.1%
-3.1108332 1
< 0.1%
-3.1096885 1
< 0.1%
-3.109084 1
< 0.1%
-3.1058936 1
< 0.1%
-3.1057773 1
< 0.1%
-3.1027234 1
< 0.1%
ValueCountFrequency (%)
3.1415927 15
0.1%
3.1268153 1
 
< 0.1%
3.1256487 1
 
< 0.1%
3.1247165 1
 
< 0.1%
3.122727 1
 
< 0.1%
3.1207623 1
 
< 0.1%
3.1203194 1
 
< 0.1%
3.119374 1
 
< 0.1%
3.118696 1
 
< 0.1%
3.116598 1
 
< 0.1%

pass_assisted_shot_id
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct124
Distinct (%)100.0%
Missing22937
Missing (%)99.5%
Memory size360.3 KiB
e44ccaea-b7c1-4d94-9e6c-827c0260c089
 
1
117fd00a-fb48-420b-801e-b1a9608cc6d1
 
1
3c1af2d0-4421-4973-86ab-8099c4952fd5
 
1
469aee5f-5fdf-44ba-97dd-5f356bf1eee7
 
1
55ed23fc-f845-4bff-9bbe-50bd444ac104
 
1
Other values (119)
119 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters4464
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)100.0%

Sample

1st rowe5ed7ea9-2f41-4355-890d-553932477e4b
2nd row313be9a2-84dc-4590-9b9a-ef79f29ef657
3rd rowb5b70899-70dc-444b-b9b7-022d8a56276b
4th row50125903-7bd5-41f4-be90-a7f8bea9c361
5th rowd1dcaff8-ed12-47ec-addb-09eb7695853d

Common Values

ValueCountFrequency (%)
e44ccaea-b7c1-4d94-9e6c-827c0260c089 1
 
< 0.1%
117fd00a-fb48-420b-801e-b1a9608cc6d1 1
 
< 0.1%
3c1af2d0-4421-4973-86ab-8099c4952fd5 1
 
< 0.1%
469aee5f-5fdf-44ba-97dd-5f356bf1eee7 1
 
< 0.1%
55ed23fc-f845-4bff-9bbe-50bd444ac104 1
 
< 0.1%
0ba4d792-4760-411e-8e86-74a59c0c0493 1
 
< 0.1%
574acbc2-476f-4e3c-9308-a6894a68ea38 1
 
< 0.1%
8a1e9d68-dff6-46e7-bffc-1db468109bd1 1
 
< 0.1%
26878481-1212-4124-a290-46fb93046f75 1
 
< 0.1%
b55815c0-f89a-434e-9b42-9287d7ff9b90 1
 
< 0.1%
Other values (114) 114
 
0.5%
(Missing) 22937
99.5%

Length

2023-02-07T18:52:38.096723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e44ccaea-b7c1-4d94-9e6c-827c0260c089 1
 
0.8%
2ccab77d-150a-4b16-b755-43f29cf81342 1
 
0.8%
313be9a2-84dc-4590-9b9a-ef79f29ef657 1
 
0.8%
b5b70899-70dc-444b-b9b7-022d8a56276b 1
 
0.8%
50125903-7bd5-41f4-be90-a7f8bea9c361 1
 
0.8%
d1dcaff8-ed12-47ec-addb-09eb7695853d 1
 
0.8%
5651223c-b283-4393-a97b-fac0cfd495e3 1
 
0.8%
a94d7eda-dcea-40c0-93a9-33249fa48466 1
 
0.8%
69d36eca-1337-449b-9b5f-3311d6ba68e3 1
 
0.8%
4059d3d3-ffef-442b-a194-8e444d9815f8 1
 
0.8%
Other values (114) 114
91.9%

Most occurring characters

ValueCountFrequency (%)
- 496
 
11.1%
4 365
 
8.2%
9 291
 
6.5%
b 267
 
6.0%
1 254
 
5.7%
d 249
 
5.6%
3 248
 
5.6%
8 245
 
5.5%
7 243
 
5.4%
0 239
 
5.4%
Other values (7) 1567
35.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2532
56.7%
Lowercase Letter 1436
32.2%
Dash Punctuation 496
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 365
14.4%
9 291
11.5%
1 254
10.0%
3 248
9.8%
8 245
9.7%
7 243
9.6%
0 239
9.4%
5 223
8.8%
6 216
8.5%
2 208
8.2%
Lowercase Letter
ValueCountFrequency (%)
b 267
18.6%
d 249
17.3%
a 239
16.6%
f 232
16.2%
c 229
15.9%
e 220
15.3%
Dash Punctuation
ValueCountFrequency (%)
- 496
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3028
67.8%
Latin 1436
32.2%

Most frequent character per script

Common
ValueCountFrequency (%)
- 496
16.4%
4 365
12.1%
9 291
9.6%
1 254
8.4%
3 248
8.2%
8 245
8.1%
7 243
8.0%
0 239
7.9%
5 223
7.4%
6 216
7.1%
Latin
ValueCountFrequency (%)
b 267
18.6%
d 249
17.3%
a 239
16.6%
f 232
16.2%
c 229
15.9%
e 220
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4464
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 496
 
11.1%
4 365
 
8.2%
9 291
 
6.5%
b 267
 
6.0%
1 254
 
5.7%
d 249
 
5.6%
3 248
 
5.6%
8 245
 
5.5%
7 243
 
5.4%
0 239
 
5.4%
Other values (7) 1567
35.1%

pass_body_part
Categorical

IMBALANCE  MISSING 

Distinct7
Distinct (%)0.1%
Missing17371
Missing (%)75.3%
Memory size360.3 KiB
Right Foot
3889 
Left Foot
1403 
Head
 
299
Drop Kick
 
44
Keeper Arm
 
40
Other values (2)
 
15

Length

Max length10
Median length10
Mean length9.4182777
Min length4

Characters and Unicode

Total characters53590
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRight Foot
2nd rowRight Foot
3rd rowRight Foot
4th rowRight Foot
5th rowHead

Common Values

ValueCountFrequency (%)
Right Foot 3889
 
16.9%
Left Foot 1403
 
6.1%
Head 299
 
1.3%
Drop Kick 44
 
0.2%
Keeper Arm 40
 
0.2%
Other 13
 
0.1%
No Touch 2
 
< 0.1%
(Missing) 17371
75.3%

Length

2023-02-07T18:52:38.187743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-07T18:52:38.482814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
foot 5292
47.8%
right 3889
35.1%
left 1403
 
12.7%
head 299
 
2.7%
drop 44
 
0.4%
kick 44
 
0.4%
keeper 40
 
0.4%
arm 40
 
0.4%
other 13
 
0.1%
no 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 10632
19.8%
t 10597
19.8%
5378
10.0%
F 5292
9.9%
i 3933
 
7.3%
h 3904
 
7.3%
R 3889
 
7.3%
g 3889
 
7.3%
e 1835
 
3.4%
L 1403
 
2.6%
Other values (16) 2838
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37144
69.3%
Uppercase Letter 11068
 
20.7%
Space Separator 5378
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 10632
28.6%
t 10597
28.5%
i 3933
 
10.6%
h 3904
 
10.5%
g 3889
 
10.5%
e 1835
 
4.9%
f 1403
 
3.8%
a 299
 
0.8%
d 299
 
0.8%
r 137
 
0.4%
Other values (5) 216
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
F 5292
47.8%
R 3889
35.1%
L 1403
 
12.7%
H 299
 
2.7%
K 84
 
0.8%
D 44
 
0.4%
A 40
 
0.4%
O 13
 
0.1%
N 2
 
< 0.1%
T 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
5378
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 48212
90.0%
Common 5378
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 10632
22.1%
t 10597
22.0%
F 5292
11.0%
i 3933
 
8.2%
h 3904
 
8.1%
R 3889
 
8.1%
g 3889
 
8.1%
e 1835
 
3.8%
L 1403
 
2.9%
f 1403
 
2.9%
Other values (15) 1435
 
3.0%
Common
ValueCountFrequency (%)
5378
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53590
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 10632
19.8%
t 10597
19.8%
5378
10.0%
F 5292
9.9%
i 3933
 
7.3%
h 3904
 
7.3%
R 3889
 
7.3%
g 3889
 
7.3%
e 1835
 
3.4%
L 1403
 
2.6%
Other values (16) 2838
 
5.3%

pass_cross
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.7%
Missing22910
Missing (%)99.3%
Memory size360.3 KiB
True
 
151
(Missing)
22910 
ValueCountFrequency (%)
True 151
 
0.7%
(Missing) 22910
99.3%
2023-02-07T18:52:38.587833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_cut_back
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)6.2%
Missing23045
Missing (%)99.9%
Memory size360.3 KiB
True
 
16
(Missing)
23045 
ValueCountFrequency (%)
True 16
 
0.1%
(Missing) 23045
99.9%
2023-02-07T18:52:38.666851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_end_location
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing16828
Missing (%)73.0%
Memory size360.3 KiB

pass_goal_assist
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)5.6%
Missing23043
Missing (%)99.9%
Memory size360.3 KiB
True
 
18
(Missing)
23043 
ValueCountFrequency (%)
True 18
 
0.1%
(Missing) 23043
99.9%
2023-02-07T18:52:38.757872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_height
Categorical

Distinct3
Distinct (%)< 0.1%
Missing16828
Missing (%)73.0%
Memory size360.3 KiB
Ground Pass
3726 
High Pass
1581 
Low Pass
926 

Length

Max length11
Median length11
Mean length10.047008
Min length8

Characters and Unicode

Total characters62623
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGround Pass
2nd rowHigh Pass
3rd rowLow Pass
4th rowLow Pass
5th rowGround Pass

Common Values

ValueCountFrequency (%)
Ground Pass 3726
 
16.2%
High Pass 1581
 
6.9%
Low Pass 926
 
4.0%
(Missing) 16828
73.0%

Length

2023-02-07T18:52:38.844891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-07T18:52:38.950904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
pass 6233
50.0%
ground 3726
29.9%
high 1581
 
12.7%
low 926
 
7.4%

Most occurring characters

ValueCountFrequency (%)
s 12466
19.9%
6233
10.0%
P 6233
10.0%
a 6233
10.0%
o 4652
 
7.4%
G 3726
 
5.9%
r 3726
 
5.9%
u 3726
 
5.9%
n 3726
 
5.9%
d 3726
 
5.9%
Other values (6) 8176
13.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 43924
70.1%
Uppercase Letter 12466
 
19.9%
Space Separator 6233
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 12466
28.4%
a 6233
14.2%
o 4652
 
10.6%
r 3726
 
8.5%
u 3726
 
8.5%
n 3726
 
8.5%
d 3726
 
8.5%
i 1581
 
3.6%
g 1581
 
3.6%
h 1581
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
P 6233
50.0%
G 3726
29.9%
H 1581
 
12.7%
L 926
 
7.4%
Space Separator
ValueCountFrequency (%)
6233
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 56390
90.0%
Common 6233
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 12466
22.1%
P 6233
11.1%
a 6233
11.1%
o 4652
 
8.2%
G 3726
 
6.6%
r 3726
 
6.6%
u 3726
 
6.6%
n 3726
 
6.6%
d 3726
 
6.6%
H 1581
 
2.8%
Other values (5) 6595
11.7%
Common
ValueCountFrequency (%)
6233
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62623
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 12466
19.9%
6233
10.0%
P 6233
10.0%
a 6233
10.0%
o 4652
 
7.4%
G 3726
 
5.9%
r 3726
 
5.9%
u 3726
 
5.9%
n 3726
 
5.9%
d 3726
 
5.9%
Other values (6) 8176
13.1%

pass_inswinging
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)2.8%
Missing23025
Missing (%)99.8%
Memory size360.3 KiB
True
 
36
(Missing)
23025 
ValueCountFrequency (%)
True 36
 
0.2%
(Missing) 23025
99.8%
2023-02-07T18:52:39.041925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_length
Real number (ℝ)

Distinct5379
Distinct (%)86.3%
Missing16828
Missing (%)73.0%
Infinite0
Infinite (%)0.0%
Mean21.952589
Minimum1.3
Maximum101.16486
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.3 KiB
2023-02-07T18:52:39.133946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile6.384667
Q112.080563
median18.101105
Q327.934208
95-th percentile51.753873
Maximum101.16486
Range99.86486
Interquartile range (IQR)15.853645

Descriptive statistics

Standard deviation14.091343
Coefficient of variation (CV)0.64189892
Kurtosis2.1142413
Mean21.952589
Median Absolute Deviation (MAD)7.037199
Skewness1.4130764
Sum136830.49
Variance198.56596
MonotonicityNot monotonic
2023-02-07T18:52:39.257975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.327723 5
 
< 0.1%
9.646243 5
 
< 0.1%
7.8492036 5
 
< 0.1%
7.4330344 5
 
< 0.1%
14.089003 4
 
< 0.1%
9.208692 4
 
< 0.1%
10.5 4
 
< 0.1%
10.547511 4
 
< 0.1%
13.888484 4
 
< 0.1%
19.331322 4
 
< 0.1%
Other values (5369) 6189
 
26.8%
(Missing) 16828
73.0%
ValueCountFrequency (%)
1.3 1
< 0.1%
1.3152946 1
< 0.1%
1.4142135 1
< 0.1%
1.4422206 1
< 0.1%
1.456022 1
< 0.1%
1.555635 1
< 0.1%
1.5652475 1
< 0.1%
1.5811388 2
< 0.1%
1.6492423 1
< 0.1%
1.6763054 1
< 0.1%
ValueCountFrequency (%)
101.16486 1
< 0.1%
91.69002 1
< 0.1%
90.27358 1
< 0.1%
87.91411 1
< 0.1%
85.40311 1
< 0.1%
85.33024 1
< 0.1%
83.35034 1
< 0.1%
82.79541 1
< 0.1%
82.211006 1
< 0.1%
82.15138 1
< 0.1%

pass_outcome
Categorical

IMBALANCE  MISSING 

Distinct4
Distinct (%)0.2%
Missing21449
Missing (%)93.0%
Memory size360.3 KiB
Incomplete
1380 
Out
176 
Unknown
 
29
Pass Offside
 
27

Length

Max length12
Median length10
Mean length9.2152605
Min length3

Characters and Unicode

Total characters14855
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIncomplete
2nd rowIncomplete
3rd rowIncomplete
4th rowIncomplete
5th rowIncomplete

Common Values

ValueCountFrequency (%)
Incomplete 1380
 
6.0%
Out 176
 
0.8%
Unknown 29
 
0.1%
Pass Offside 27
 
0.1%
(Missing) 21449
93.0%

Length

2023-02-07T18:52:39.368999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-07T18:52:39.469022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
incomplete 1380
84.2%
out 176
 
10.7%
unknown 29
 
1.8%
pass 27
 
1.6%
offside 27
 
1.6%

Most occurring characters

ValueCountFrequency (%)
e 2787
18.8%
t 1556
10.5%
n 1467
9.9%
o 1409
9.5%
I 1380
9.3%
c 1380
9.3%
m 1380
9.3%
p 1380
9.3%
l 1380
9.3%
O 203
 
1.4%
Other values (11) 533
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13189
88.8%
Uppercase Letter 1639
 
11.0%
Space Separator 27
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2787
21.1%
t 1556
11.8%
n 1467
11.1%
o 1409
10.7%
c 1380
10.5%
m 1380
10.5%
p 1380
10.5%
l 1380
10.5%
u 176
 
1.3%
s 81
 
0.6%
Other values (6) 193
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
I 1380
84.2%
O 203
 
12.4%
U 29
 
1.8%
P 27
 
1.6%
Space Separator
ValueCountFrequency (%)
27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14828
99.8%
Common 27
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2787
18.8%
t 1556
10.5%
n 1467
9.9%
o 1409
9.5%
I 1380
9.3%
c 1380
9.3%
m 1380
9.3%
p 1380
9.3%
l 1380
9.3%
O 203
 
1.4%
Other values (10) 506
 
3.4%
Common
ValueCountFrequency (%)
27
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14855
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2787
18.8%
t 1556
10.5%
n 1467
9.9%
o 1409
9.5%
I 1380
9.3%
c 1380
9.3%
m 1380
9.3%
p 1380
9.3%
l 1380
9.3%
O 203
 
1.4%
Other values (11) 533
 
3.6%

pass_outswinging
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)12.5%
Missing23053
Missing (%)> 99.9%
Memory size360.3 KiB
True
 
8
(Missing)
23053 
ValueCountFrequency (%)
True 8
 
< 0.1%
(Missing) 23053
> 99.9%
2023-02-07T18:52:39.563053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_recipient
Categorical

HIGH CARDINALITY  MISSING 

Distinct117
Distinct (%)2.0%
Missing17339
Missing (%)75.2%
Memory size360.3 KiB
Abby Dahlkemper
 
300
Megan Anna Rapinoe
 
289
Samantha June Mewis
 
266
Lindsey Michelle Horan
 
253
Alexandra Morgan Carrasco
 
247
Other values (112)
4367 

Length

Max length38
Median length31
Mean length19.543866
Min length10

Characters and Unicode

Total characters111830
Distinct characters60
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAbby Dahlkemper
2nd rowSamantha June Mewis
3rd rowAbby Dahlkemper
4th rowJulie Beth Ertz
5th rowMegan Anna Rapinoe

Common Values

ValueCountFrequency (%)
Abby Dahlkemper 300
 
1.3%
Megan Anna Rapinoe 289
 
1.3%
Samantha June Mewis 266
 
1.2%
Lindsey Michelle Horan 253
 
1.1%
Alexandra Morgan Carrasco 247
 
1.1%
Crystal Alyssia Dunn Soubrier 240
 
1.0%
Rebecca Elizabeth Sauerbrunn 234
 
1.0%
Kelley Maureen O''Hara 232
 
1.0%
Tobin Powell Heath 230
 
1.0%
Rosemary Kathleen Lavelle 166
 
0.7%
Other values (107) 3265
 
14.2%
(Missing) 17339
75.2%

Length

2023-02-07T18:52:39.651063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
anna 321
 
2.0%
abby 300
 
1.9%
dahlkemper 300
 
1.9%
morgan 297
 
1.9%
megan 289
 
1.8%
rapinoe 289
 
1.8%
samantha 266
 
1.7%
june 266
 
1.7%
mewis 266
 
1.7%
lindsey 253
 
1.6%
Other values (285) 12982
82.0%

Most occurring characters

ValueCountFrequency (%)
e 12183
 
10.9%
a 12008
 
10.7%
10107
 
9.0%
n 8817
 
7.9%
r 7352
 
6.6%
i 6612
 
5.9%
l 6061
 
5.4%
o 4080
 
3.6%
s 3759
 
3.4%
t 3461
 
3.1%
Other values (50) 37390
33.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 85247
76.2%
Uppercase Letter 15946
 
14.3%
Space Separator 10107
 
9.0%
Other Punctuation 464
 
0.4%
Dash Punctuation 66
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12183
14.3%
a 12008
14.1%
n 8817
10.3%
r 7352
8.6%
i 6612
 
7.8%
l 6061
 
7.1%
o 4080
 
4.8%
s 3759
 
4.4%
t 3461
 
4.1%
h 2929
 
3.4%
Other values (23) 17985
21.1%
Uppercase Letter
ValueCountFrequency (%)
A 1940
12.2%
M 1901
11.9%
S 1508
 
9.5%
H 1056
 
6.6%
C 1034
 
6.5%
D 956
 
6.0%
R 933
 
5.9%
L 895
 
5.6%
J 822
 
5.2%
P 820
 
5.1%
Other values (14) 4081
25.6%
Space Separator
ValueCountFrequency (%)
10107
100.0%
Other Punctuation
ValueCountFrequency (%)
' 464
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 101193
90.5%
Common 10637
 
9.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 12183
 
12.0%
a 12008
 
11.9%
n 8817
 
8.7%
r 7352
 
7.3%
i 6612
 
6.5%
l 6061
 
6.0%
o 4080
 
4.0%
s 3759
 
3.7%
t 3461
 
3.4%
h 2929
 
2.9%
Other values (47) 33931
33.5%
Common
ValueCountFrequency (%)
10107
95.0%
' 464
 
4.4%
- 66
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111268
99.5%
None 562
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 12183
 
10.9%
a 12008
 
10.8%
10107
 
9.1%
n 8817
 
7.9%
r 7352
 
6.6%
i 6612
 
5.9%
l 6061
 
5.4%
o 4080
 
3.7%
s 3759
 
3.4%
t 3461
 
3.1%
Other values (43) 36828
33.1%
None
ValueCountFrequency (%)
é 181
32.2%
í 176
31.3%
ó 83
14.8%
ö 45
 
8.0%
ë 44
 
7.8%
á 27
 
4.8%
ñ 6
 
1.1%

pass_shot_assist
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.9%
Missing22955
Missing (%)99.5%
Memory size360.3 KiB
True
 
106
(Missing)
22955 
ValueCountFrequency (%)
True 106
 
0.5%
(Missing) 22955
99.5%
2023-02-07T18:52:39.746095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_straight
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)9.1%
Missing23050
Missing (%)> 99.9%
Memory size360.3 KiB
True
 
11
(Missing)
23050 
ValueCountFrequency (%)
True 11
 
< 0.1%
(Missing) 23050
> 99.9%
2023-02-07T18:52:39.815110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_switch
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.7%
Missing22910
Missing (%)99.3%
Memory size360.3 KiB
True
 
151
(Missing)
22910 
ValueCountFrequency (%)
True 151
 
0.7%
(Missing) 22910
99.3%
2023-02-07T18:52:39.888127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_technique
Categorical

Distinct4
Distinct (%)4.8%
Missing22978
Missing (%)99.6%
Memory size360.3 KiB
Inswinging
36 
Through Ball
28 
Straight
11 
Outswinging
8 

Length

Max length12
Median length11
Mean length10.506024
Min length8

Characters and Unicode

Total characters872
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThrough Ball
2nd rowInswinging
3rd rowStraight
4th rowInswinging
5th rowThrough Ball

Common Values

ValueCountFrequency (%)
Inswinging 36
 
0.2%
Through Ball 28
 
0.1%
Straight 11
 
< 0.1%
Outswinging 8
 
< 0.1%
(Missing) 22978
99.6%

Length

2023-02-07T18:52:39.971146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-07T18:52:40.083171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
inswinging 36
32.4%
through 28
25.2%
ball 28
25.2%
straight 11
 
9.9%
outswinging 8
 
7.2%

Most occurring characters

ValueCountFrequency (%)
g 127
14.6%
n 124
14.2%
i 99
11.4%
h 67
 
7.7%
l 56
 
6.4%
s 44
 
5.0%
w 44
 
5.0%
a 39
 
4.5%
r 39
 
4.5%
I 36
 
4.1%
Other values (8) 197
22.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 733
84.1%
Uppercase Letter 111
 
12.7%
Space Separator 28
 
3.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
g 127
17.3%
n 124
16.9%
i 99
13.5%
h 67
9.1%
l 56
7.6%
s 44
 
6.0%
w 44
 
6.0%
a 39
 
5.3%
r 39
 
5.3%
u 36
 
4.9%
Other values (2) 58
7.9%
Uppercase Letter
ValueCountFrequency (%)
I 36
32.4%
B 28
25.2%
T 28
25.2%
S 11
 
9.9%
O 8
 
7.2%
Space Separator
ValueCountFrequency (%)
28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 844
96.8%
Common 28
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
g 127
15.0%
n 124
14.7%
i 99
11.7%
h 67
 
7.9%
l 56
 
6.6%
s 44
 
5.2%
w 44
 
5.2%
a 39
 
4.6%
r 39
 
4.6%
I 36
 
4.3%
Other values (7) 169
20.0%
Common
ValueCountFrequency (%)
28
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 872
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
g 127
14.6%
n 124
14.2%
i 99
11.4%
h 67
 
7.7%
l 56
 
6.4%
s 44
 
5.0%
w 44
 
5.0%
a 39
 
4.5%
r 39
 
4.5%
I 36
 
4.1%
Other values (8) 197
22.6%

pass_through_ball
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)3.6%
Missing23033
Missing (%)99.9%
Memory size360.3 KiB
True
 
28
(Missing)
23033 
ValueCountFrequency (%)
True 28
 
0.1%
(Missing) 23033
99.9%
2023-02-07T18:52:40.177192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_type
Categorical

Distinct7
Distinct (%)0.5%
Missing21608
Missing (%)93.7%
Memory size360.3 KiB
Recovery
682 
Throw-in
387 
Free Kick
150 
Goal Kick
111 
Corner
 
60
Other values (2)
 
63

Length

Max length12
Median length8
Mean length8.1520991
Min length6

Characters and Unicode

Total characters11845
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKick Off
2nd rowThrow-in
3rd rowRecovery
4th rowRecovery
5th rowThrow-in

Common Values

ValueCountFrequency (%)
Recovery 682
 
3.0%
Throw-in 387
 
1.7%
Free Kick 150
 
0.7%
Goal Kick 111
 
0.5%
Corner 60
 
0.3%
Kick Off 43
 
0.2%
Interception 20
 
0.1%
(Missing) 21608
93.7%

Length

2023-02-07T18:52:40.251209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-07T18:52:40.359233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
recovery 682
38.8%
throw-in 387
22.0%
kick 304
17.3%
free 150
 
8.5%
goal 111
 
6.3%
corner 60
 
3.4%
off 43
 
2.4%
interception 20
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e 1764
14.9%
r 1359
11.5%
o 1260
10.6%
c 1006
 
8.5%
i 711
 
6.0%
R 682
 
5.8%
v 682
 
5.8%
y 682
 
5.8%
n 487
 
4.1%
T 387
 
3.3%
Other values (16) 2825
23.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9397
79.3%
Uppercase Letter 1757
 
14.8%
Dash Punctuation 387
 
3.3%
Space Separator 304
 
2.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1764
18.8%
r 1359
14.5%
o 1260
13.4%
c 1006
10.7%
i 711
7.6%
v 682
 
7.3%
y 682
 
7.3%
n 487
 
5.2%
h 387
 
4.1%
w 387
 
4.1%
Other values (6) 672
 
7.2%
Uppercase Letter
ValueCountFrequency (%)
R 682
38.8%
T 387
22.0%
K 304
17.3%
F 150
 
8.5%
G 111
 
6.3%
C 60
 
3.4%
O 43
 
2.4%
I 20
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 387
100.0%
Space Separator
ValueCountFrequency (%)
304
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11154
94.2%
Common 691
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1764
15.8%
r 1359
12.2%
o 1260
11.3%
c 1006
9.0%
i 711
 
6.4%
R 682
 
6.1%
v 682
 
6.1%
y 682
 
6.1%
n 487
 
4.4%
T 387
 
3.5%
Other values (14) 2134
19.1%
Common
ValueCountFrequency (%)
- 387
56.0%
304
44.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11845
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1764
14.9%
r 1359
11.5%
o 1260
10.6%
c 1006
 
8.5%
i 711
 
6.0%
R 682
 
5.8%
v 682
 
5.8%
y 682
 
5.8%
n 487
 
4.1%
T 387
 
3.3%
Other values (16) 2825
23.8%

period
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size360.3 KiB
1
11543 
2
11518 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23061
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 11543
50.1%
2 11518
49.9%

Length

2023-02-07T18:52:40.460256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-07T18:52:40.543275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 11543
50.1%
2 11518
49.9%

Most occurring characters

ValueCountFrequency (%)
1 11543
50.1%
2 11518
49.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23061
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11543
50.1%
2 11518
49.9%

Most occurring scripts

ValueCountFrequency (%)
Common 23061
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 11543
50.1%
2 11518
49.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 11543
50.1%
2 11518
49.9%

play_pattern
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size360.3 KiB
Regular Play
10587 
From Throw In
6647 
From Free Kick
1953 
From Goal Kick
1445 
From Corner
 
963
Other values (4)
1466 

Length

Max length14
Median length13
Mean length12.503447
Min length5

Characters and Unicode

Total characters288342
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRegular Play
2nd rowRegular Play
3rd rowRegular Play
4th rowRegular Play
5th rowFrom Free Kick

Common Values

ValueCountFrequency (%)
Regular Play 10587
45.9%
From Throw In 6647
28.8%
From Free Kick 1953
 
8.5%
From Goal Kick 1445
 
6.3%
From Corner 963
 
4.2%
From Keeper 636
 
2.8%
From Kick Off 557
 
2.4%
From Counter 160
 
0.7%
Other 113
 
0.5%

Length

2023-02-07T18:52:40.619292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-07T18:52:40.724315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
from 12361
21.8%
regular 10587
18.7%
play 10587
18.7%
throw 6647
11.7%
in 6647
11.7%
kick 3955
 
7.0%
free 1953
 
3.4%
goal 1445
 
2.6%
corner 963
 
1.7%
keeper 636
 
1.1%
Other values (3) 830
 
1.5%

Most occurring characters

ValueCountFrequency (%)
r 34383
 
11.9%
33550
 
11.6%
l 22619
 
7.8%
a 22619
 
7.8%
o 21576
 
7.5%
e 17637
 
6.1%
F 14314
 
5.0%
m 12361
 
4.3%
u 10747
 
3.7%
R 10587
 
3.7%
Other values (18) 87949
30.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 198181
68.7%
Uppercase Letter 56611
 
19.6%
Space Separator 33550
 
11.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 34383
17.3%
l 22619
11.4%
a 22619
11.4%
o 21576
10.9%
e 17637
8.9%
m 12361
 
6.2%
u 10747
 
5.4%
g 10587
 
5.3%
y 10587
 
5.3%
n 7770
 
3.9%
Other values (8) 27295
13.8%
Uppercase Letter
ValueCountFrequency (%)
F 14314
25.3%
R 10587
18.7%
P 10587
18.7%
I 6647
11.7%
T 6647
11.7%
K 4591
 
8.1%
G 1445
 
2.6%
C 1123
 
2.0%
O 670
 
1.2%
Space Separator
ValueCountFrequency (%)
33550
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 254792
88.4%
Common 33550
 
11.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 34383
13.5%
l 22619
 
8.9%
a 22619
 
8.9%
o 21576
 
8.5%
e 17637
 
6.9%
F 14314
 
5.6%
m 12361
 
4.9%
u 10747
 
4.2%
R 10587
 
4.2%
g 10587
 
4.2%
Other values (17) 77362
30.4%
Common
ValueCountFrequency (%)
33550
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 288342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 34383
 
11.9%
33550
 
11.6%
l 22619
 
7.8%
a 22619
 
7.8%
o 21576
 
7.5%
e 17637
 
6.1%
F 14314
 
5.0%
m 12361
 
4.3%
u 10747
 
3.7%
R 10587
 
3.7%
Other values (18) 87949
30.5%

player
Categorical

Distinct117
Distinct (%)0.5%
Missing93
Missing (%)0.4%
Memory size360.3 KiB
Crystal Alyssia Dunn Soubrier
 
1134
Abby Dahlkemper
 
1110
Samantha June Mewis
 
980
Megan Anna Rapinoe
 
978
Kelley Maureen O''Hara
 
970
Other values (112)
17796 

Length

Max length38
Median length30
Mean length19.820228
Min length10

Characters and Unicode

Total characters455231
Distinct characters60
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLindsey Michelle Horan
2nd rowAbby Dahlkemper
3rd rowSamantha June Mewis
4th rowKelley Maureen O''Hara
5th rowAbby Dahlkemper

Common Values

ValueCountFrequency (%)
Crystal Alyssia Dunn Soubrier 1134
 
4.9%
Abby Dahlkemper 1110
 
4.8%
Samantha June Mewis 980
 
4.2%
Megan Anna Rapinoe 978
 
4.2%
Kelley Maureen O''Hara 970
 
4.2%
Lindsey Michelle Horan 905
 
3.9%
Rebecca Elizabeth Sauerbrunn 897
 
3.9%
Tobin Powell Heath 810
 
3.5%
Rosemary Kathleen Lavelle 781
 
3.4%
Alexandra Morgan Carrasco 751
 
3.3%
Other values (107) 13652
59.2%

Length

2023-02-07T18:52:40.848344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
crystal 1134
 
1.8%
soubrier 1134
 
1.8%
alyssia 1134
 
1.8%
dunn 1134
 
1.8%
anna 1129
 
1.8%
abby 1110
 
1.7%
dahlkemper 1110
 
1.7%
samantha 980
 
1.5%
june 980
 
1.5%
mewis 980
 
1.5%
Other values (285) 53344
83.1%

Most occurring characters

ValueCountFrequency (%)
e 49279
 
10.8%
a 48981
 
10.8%
41201
 
9.1%
n 35273
 
7.7%
r 30165
 
6.6%
i 26978
 
5.9%
l 25115
 
5.5%
o 16570
 
3.6%
s 15723
 
3.5%
t 14287
 
3.1%
Other values (50) 151659
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 347004
76.2%
Uppercase Letter 64719
 
14.2%
Space Separator 41201
 
9.1%
Other Punctuation 1940
 
0.4%
Dash Punctuation 367
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 49279
14.2%
a 48981
14.1%
n 35273
10.2%
r 30165
8.7%
i 26978
 
7.8%
l 25115
 
7.2%
o 16570
 
4.8%
s 15723
 
4.5%
t 14287
 
4.1%
h 12235
 
3.5%
Other values (23) 72398
20.9%
Uppercase Letter
ValueCountFrequency (%)
A 7606
11.8%
M 7294
11.3%
S 6316
 
9.8%
C 4507
 
7.0%
H 4162
 
6.4%
D 3900
 
6.0%
R 3772
 
5.8%
L 3689
 
5.7%
J 3298
 
5.1%
P 3175
 
4.9%
Other values (14) 17000
26.3%
Space Separator
ValueCountFrequency (%)
41201
100.0%
Other Punctuation
ValueCountFrequency (%)
' 1940
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 367
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 411723
90.4%
Common 43508
 
9.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 49279
 
12.0%
a 48981
 
11.9%
n 35273
 
8.6%
r 30165
 
7.3%
i 26978
 
6.6%
l 25115
 
6.1%
o 16570
 
4.0%
s 15723
 
3.8%
t 14287
 
3.5%
h 12235
 
3.0%
Other values (47) 137117
33.3%
Common
ValueCountFrequency (%)
41201
94.7%
' 1940
 
4.5%
- 367
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 453089
99.5%
None 2142
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 49279
 
10.9%
a 48981
 
10.8%
41201
 
9.1%
n 35273
 
7.8%
r 30165
 
6.7%
i 26978
 
6.0%
l 25115
 
5.5%
o 16570
 
3.7%
s 15723
 
3.5%
t 14287
 
3.2%
Other values (43) 149517
33.0%
None
ValueCountFrequency (%)
í 648
30.3%
é 562
26.2%
ó 370
17.3%
ö 219
 
10.2%
ë 158
 
7.4%
á 121
 
5.6%
ñ 64
 
3.0%

player_id
Real number (ℝ)

Distinct117
Distinct (%)0.5%
Missing93
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean10150.721
Minimum4640
Maximum26094
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.3 KiB
2023-02-07T18:52:40.964370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4640
5-th percentile4949
Q15081
median6813
Q310645
95-th percentile25693
Maximum26094
Range21454
Interquartile range (IQR)5564

Descriptive statistics

Standard deviation7277.5064
Coefficient of variation (CV)0.7169448
Kurtosis0.47118485
Mean10150.721
Median Absolute Deviation (MAD)1814
Skewness1.4065163
Sum2.3314175 × 108
Variance52962100
MonotonicityNot monotonic
2023-02-07T18:52:41.081386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5088 1134
 
4.9%
5081 1110
 
4.8%
5087 980
 
4.2%
8298 978
 
4.2%
5021 970
 
4.2%
4999 905
 
3.9%
5030 897
 
3.9%
5013 810
 
3.5%
4949 781
 
3.4%
5085 751
 
3.3%
Other values (107) 13652
59.2%
ValueCountFrequency (%)
4640 94
 
0.4%
4641 55
 
0.2%
4642 172
 
0.7%
4643 7
 
< 0.1%
4651 116
 
0.5%
4654 148
 
0.6%
4658 161
 
0.7%
4949 781
3.4%
4999 905
3.9%
5001 69
 
0.3%
ValueCountFrequency (%)
26094 164
0.7%
26093 144
0.6%
26092 122
0.5%
25842 27
 
0.1%
25834 15
 
0.1%
25817 43
 
0.2%
25816 265
1.1%
25815 34
 
0.1%
25814 9
 
< 0.1%
25697 26
 
0.1%

position
Categorical

Distinct21
Distinct (%)0.1%
Missing93
Missing (%)0.4%
Memory size360.3 KiB
Right Back
2303 
Left Wing
2192 
Left Back
2184 
Right Center Back
2098 
Left Center Midfield
2029 
Other values (16)
12162 

Length

Max length25
Median length23
Mean length15.10175
Min length9

Characters and Unicode

Total characters346857
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCenter Defensive Midfield
2nd rowRight Center Back
3rd rowLeft Center Midfield
4th rowRight Back
5th rowRight Center Back

Common Values

ValueCountFrequency (%)
Right Back 2303
10.0%
Left Wing 2192
9.5%
Left Back 2184
9.5%
Right Center Back 2098
9.1%
Left Center Midfield 2029
8.8%
Left Center Back 2001
8.7%
Right Wing 1965
8.5%
Right Center Midfield 1946
8.4%
Center Forward 1600
6.9%
Center Defensive Midfield 1299
 
5.6%
Other values (11) 3351
14.5%

Length

2023-02-07T18:52:41.197423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
center 12079
21.4%
left 9280
16.4%
right 9063
16.1%
back 8840
15.7%
midfield 7161
12.7%
wing 4306
 
7.6%
defensive 2203
 
3.9%
forward 1895
 
3.4%
goalkeeper 915
 
1.6%
attacking 706
 
1.3%

Most occurring characters

ValueCountFrequency (%)
e 49953
14.4%
33480
 
9.7%
t 31834
 
9.2%
i 30600
 
8.8%
n 19294
 
5.6%
f 18644
 
5.4%
r 16784
 
4.8%
d 16217
 
4.7%
g 14075
 
4.1%
a 12356
 
3.6%
Other values (19) 103620
29.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 256929
74.1%
Uppercase Letter 56448
 
16.3%
Space Separator 33480
 
9.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 49953
19.4%
t 31834
12.4%
i 30600
11.9%
n 19294
 
7.5%
f 18644
 
7.3%
r 16784
 
6.5%
d 16217
 
6.3%
g 14075
 
5.5%
a 12356
 
4.8%
k 10461
 
4.1%
Other values (8) 36711
14.3%
Uppercase Letter
ValueCountFrequency (%)
C 12079
21.4%
L 9280
16.4%
R 9063
16.1%
B 8840
15.7%
M 7161
12.7%
W 4306
 
7.6%
D 2203
 
3.9%
F 1895
 
3.4%
G 915
 
1.6%
A 706
 
1.3%
Space Separator
ValueCountFrequency (%)
33480
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 313377
90.3%
Common 33480
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 49953
15.9%
t 31834
 
10.2%
i 30600
 
9.8%
n 19294
 
6.2%
f 18644
 
5.9%
r 16784
 
5.4%
d 16217
 
5.2%
g 14075
 
4.5%
a 12356
 
3.9%
C 12079
 
3.9%
Other values (18) 91541
29.2%
Common
ValueCountFrequency (%)
33480
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 346857
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 49953
14.4%
33480
 
9.7%
t 31834
 
9.2%
i 30600
 
8.8%
n 19294
 
5.6%
f 18644
 
5.4%
r 16784
 
4.8%
d 16217
 
4.7%
g 14075
 
4.1%
a 12356
 
3.6%
Other values (19) 103620
29.9%

possession
Real number (ℝ)

Distinct215
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.757816
Minimum1
Maximum215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.3 KiB
2023-02-07T18:52:41.311448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q151
median95
Q3142
95-th percentile189
Maximum215
Range214
Interquartile range (IQR)91

Descriptive statistics

Standard deviation55.342974
Coefficient of variation (CV)0.57197419
Kurtosis-1.025754
Mean96.757816
Median Absolute Deviation (MAD)46
Skewness0.11736081
Sum2231332
Variance3062.8448
MonotonicityNot monotonic
2023-02-07T18:52:41.433465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 268
 
1.2%
28 243
 
1.1%
83 226
 
1.0%
78 224
 
1.0%
131 219
 
0.9%
58 215
 
0.9%
62 209
 
0.9%
106 208
 
0.9%
152 204
 
0.9%
105 197
 
0.9%
Other values (205) 20848
90.4%
ValueCountFrequency (%)
1 28
 
0.1%
2 123
0.5%
3 100
0.4%
4 86
0.4%
5 98
0.4%
6 64
 
0.3%
7 185
0.8%
8 57
 
0.2%
9 191
0.8%
10 78
0.3%
ValueCountFrequency (%)
215 4
 
< 0.1%
214 25
 
0.1%
213 14
 
0.1%
212 98
0.4%
211 36
 
0.2%
210 21
 
0.1%
209 22
 
0.1%
208 29
 
0.1%
207 26
 
0.1%
206 20
 
0.1%

possession_team
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size360.3 KiB
United States Women's
13254 
France Women's
1956 
England Women's
1837 
Netherlands Women's
1467 
Sweden Women's
1457 
Other values (3)
3090 

Length

Max length21
Median length21
Mean length18.391874
Min length13

Characters and Unicode

Total characters424135
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnited States Women's
2nd rowUnited States Women's
3rd rowUnited States Women's
4th rowUnited States Women's
5th rowThailand Women's

Common Values

ValueCountFrequency (%)
United States Women's 13254
57.5%
France Women's 1956
 
8.5%
England Women's 1837
 
8.0%
Netherlands Women's 1467
 
6.4%
Sweden Women's 1457
 
6.3%
Spain Women's 1333
 
5.8%
Chile Women's 950
 
4.1%
Thailand Women's 807
 
3.5%

Length

2023-02-07T18:52:41.733544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-07T18:52:41.845569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
women's 23061
38.8%
united 13254
22.3%
states 13254
22.3%
france 1956
 
3.3%
england 1837
 
3.1%
netherlands 1467
 
2.5%
sweden 1457
 
2.5%
spain 1333
 
2.2%
chile 950
 
1.6%
thailand 807
 
1.4%

Most occurring characters

ValueCountFrequency (%)
e 58323
13.8%
n 47009
11.1%
t 41229
9.7%
s 37782
8.9%
36315
8.6%
' 23061
 
5.4%
W 23061
 
5.4%
o 23061
 
5.4%
m 23061
 
5.4%
a 21461
 
5.1%
Other values (16) 89772
21.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 305383
72.0%
Uppercase Letter 59376
 
14.0%
Space Separator 36315
 
8.6%
Other Punctuation 23061
 
5.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 58323
19.1%
n 47009
15.4%
t 41229
13.5%
s 37782
12.4%
o 23061
 
7.6%
m 23061
 
7.6%
a 21461
 
7.0%
d 18822
 
6.2%
i 16344
 
5.4%
l 5061
 
1.7%
Other values (6) 13230
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
W 23061
38.8%
S 16044
27.0%
U 13254
22.3%
F 1956
 
3.3%
E 1837
 
3.1%
N 1467
 
2.5%
C 950
 
1.6%
T 807
 
1.4%
Space Separator
ValueCountFrequency (%)
36315
100.0%
Other Punctuation
ValueCountFrequency (%)
' 23061
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 364759
86.0%
Common 59376
 
14.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 58323
16.0%
n 47009
12.9%
t 41229
11.3%
s 37782
10.4%
W 23061
 
6.3%
o 23061
 
6.3%
m 23061
 
6.3%
a 21461
 
5.9%
d 18822
 
5.2%
i 16344
 
4.5%
Other values (14) 54606
15.0%
Common
ValueCountFrequency (%)
36315
61.2%
' 23061
38.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 424135
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 58323
13.8%
n 47009
11.1%
t 41229
9.7%
s 37782
8.9%
36315
8.6%
' 23061
 
5.4%
W 23061
 
5.4%
o 23061
 
5.4%
m 23061
 
5.4%
a 21461
 
5.1%
Other values (16) 89772
21.2%

possession_team_id
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1086.435
Minimum851
Maximum1214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.3 KiB
2023-02-07T18:52:41.943591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum851
5-th percentile851
Q1863
median1214
Q31214
95-th percentile1214
Maximum1214
Range363
Interquartile range (IQR)351

Descriptive statistics

Standard deviation167.07077
Coefficient of variation (CV)0.15377889
Kurtosis-1.6091104
Mean1086.435
Median Absolute Deviation (MAD)0
Skewness-0.59953731
Sum25054278
Variance27912.643
MonotonicityNot monotonic
2023-02-07T18:52:42.023598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1214 13254
57.5%
861 1956
 
8.5%
865 1837
 
8.0%
851 1467
 
6.4%
858 1457
 
6.3%
863 1333
 
5.8%
1209 950
 
4.1%
1107 807
 
3.5%
ValueCountFrequency (%)
851 1467
 
6.4%
858 1457
 
6.3%
861 1956
 
8.5%
863 1333
 
5.8%
865 1837
 
8.0%
1107 807
 
3.5%
1209 950
 
4.1%
1214 13254
57.5%
ValueCountFrequency (%)
1214 13254
57.5%
1209 950
 
4.1%
1107 807
 
3.5%
865 1837
 
8.0%
863 1333
 
5.8%
861 1956
 
8.5%
858 1457
 
6.3%
851 1467
 
6.4%

related_events
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1027
Missing (%)4.5%
Memory size360.3 KiB

second
Real number (ℝ)

Distinct60
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.034387
Minimum0
Maximum59
Zeros453
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size360.3 KiB
2023-02-07T18:52:42.138624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q114
median29
Q344
95-th percentile57
Maximum59
Range59
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.434956
Coefficient of variation (CV)0.60049335
Kurtosis-1.2106772
Mean29.034387
Median Absolute Deviation (MAD)15
Skewness0.027183805
Sum669562
Variance303.9777
MonotonicityNot monotonic
2023-02-07T18:52:42.254651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 458
 
2.0%
0 453
 
2.0%
22 437
 
1.9%
44 431
 
1.9%
17 426
 
1.8%
36 423
 
1.8%
14 418
 
1.8%
57 416
 
1.8%
15 416
 
1.8%
5 415
 
1.8%
Other values (50) 18768
81.4%
ValueCountFrequency (%)
0 453
2.0%
1 393
1.7%
2 458
2.0%
3 393
1.7%
4 415
1.8%
5 415
1.8%
6 355
1.5%
7 361
1.6%
8 414
1.8%
9 389
1.7%
ValueCountFrequency (%)
59 368
1.6%
58 370
1.6%
57 416
1.8%
56 369
1.6%
55 382
1.7%
54 341
1.5%
53 399
1.7%
52 364
1.6%
51 331
1.4%
50 374
1.6%

shot_aerial_won
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)6.2%
Missing23045
Missing (%)99.9%
Memory size360.3 KiB
True
 
16
(Missing)
23045 
ValueCountFrequency (%)
True 16
 
0.1%
(Missing) 23045
99.9%
2023-02-07T18:52:42.366678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

shot_body_part
Categorical

Distinct4
Distinct (%)2.3%
Missing22884
Missing (%)99.2%
Memory size360.3 KiB
Right Foot
81 
Left Foot
60 
Head
35 
Other
 
1

Length

Max length10
Median length9
Mean length8.4463277
Min length4

Characters and Unicode

Total characters1495
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st rowLeft Foot
2nd rowRight Foot
3rd rowHead
4th rowRight Foot
5th rowHead

Common Values

ValueCountFrequency (%)
Right Foot 81
 
0.4%
Left Foot 60
 
0.3%
Head 35
 
0.2%
Other 1
 
< 0.1%
(Missing) 22884
99.2%

Length

2023-02-07T18:52:42.449705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-07T18:52:42.556729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
foot 141
44.3%
right 81
25.5%
left 60
18.9%
head 35
 
11.0%
other 1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
t 283
18.9%
o 282
18.9%
141
9.4%
F 141
9.4%
e 96
 
6.4%
h 82
 
5.5%
R 81
 
5.4%
i 81
 
5.4%
g 81
 
5.4%
L 60
 
4.0%
Other values (6) 167
11.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1036
69.3%
Uppercase Letter 318
 
21.3%
Space Separator 141
 
9.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 283
27.3%
o 282
27.2%
e 96
 
9.3%
h 82
 
7.9%
i 81
 
7.8%
g 81
 
7.8%
f 60
 
5.8%
a 35
 
3.4%
d 35
 
3.4%
r 1
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
F 141
44.3%
R 81
25.5%
L 60
18.9%
H 35
 
11.0%
O 1
 
0.3%
Space Separator
ValueCountFrequency (%)
141
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1354
90.6%
Common 141
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 283
20.9%
o 282
20.8%
F 141
10.4%
e 96
 
7.1%
h 82
 
6.1%
R 81
 
6.0%
i 81
 
6.0%
g 81
 
6.0%
L 60
 
4.4%
f 60
 
4.4%
Other values (5) 107
 
7.9%
Common
ValueCountFrequency (%)
141
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 283
18.9%
o 282
18.9%
141
9.4%
F 141
9.4%
e 96
 
6.4%
h 82
 
5.5%
R 81
 
5.4%
i 81
 
5.4%
g 81
 
5.4%
L 60
 
4.0%
Other values (6) 167
11.2%

shot_deflected
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing23059
Missing (%)> 99.9%
Memory size360.3 KiB
True
 
2
(Missing)
23059 
ValueCountFrequency (%)
True 2
 
< 0.1%
(Missing) 23059
> 99.9%
2023-02-07T18:52:42.655743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

shot_end_location
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing22884
Missing (%)99.2%
Memory size360.3 KiB

shot_first_time
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)1.8%
Missing23005
Missing (%)99.8%
Memory size360.3 KiB
True
 
56
(Missing)
23005 
ValueCountFrequency (%)
True 56
 
0.2%
(Missing) 23005
99.8%
2023-02-07T18:52:42.731759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

shot_freeze_frame
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing22888
Missing (%)99.2%
Memory size360.3 KiB

shot_key_pass_id
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct124
Distinct (%)100.0%
Missing22937
Missing (%)99.5%
Memory size360.3 KiB
7e13948c-2627-4213-980a-5b94c2ef9b4f
 
1
3128dd59-72de-47a2-bcad-dc1bb8669789
 
1
cae1d95d-0496-45bc-bb4d-fe3452cdc907
 
1
37764e9a-e6bd-4b67-9c47-9adf43549f87
 
1
62cb8f79-d924-42bf-9c12-e7298b98efdf
 
1
Other values (119)
119 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters4464
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)100.0%

Sample

1st rowcafc0f58-145f-4c4b-8bdc-4b4e95662505
2nd row6c26dcd3-8766-47a8-b7fa-d75442d69f59
3rd row44a95676-39ce-4c49-9339-80e4809b2485
4th rowf44783eb-4040-43c2-aac4-23f59b2e3507
5th row85dc4bbd-9867-435a-ab00-a1262d1717d6

Common Values

ValueCountFrequency (%)
7e13948c-2627-4213-980a-5b94c2ef9b4f 1
 
< 0.1%
3128dd59-72de-47a2-bcad-dc1bb8669789 1
 
< 0.1%
cae1d95d-0496-45bc-bb4d-fe3452cdc907 1
 
< 0.1%
37764e9a-e6bd-4b67-9c47-9adf43549f87 1
 
< 0.1%
62cb8f79-d924-42bf-9c12-e7298b98efdf 1
 
< 0.1%
e3e9459e-ce13-4bcd-b0a3-582bad2ef658 1
 
< 0.1%
1ff0c679-de30-43c8-b573-9e9d4d4e0f66 1
 
< 0.1%
87a2f823-0817-44bc-a172-6f28f4e5a14a 1
 
< 0.1%
39e1d63a-efdd-4130-98fd-189f9b099258 1
 
< 0.1%
8f5fb98c-42e4-4d53-8077-2b19a4f32f17 1
 
< 0.1%
Other values (114) 114
 
0.5%
(Missing) 22937
99.5%

Length

2023-02-07T18:52:42.802785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7e13948c-2627-4213-980a-5b94c2ef9b4f 1
 
0.8%
2db13d4b-28db-450d-98cd-0c5f98fcd6ab 1
 
0.8%
6c26dcd3-8766-47a8-b7fa-d75442d69f59 1
 
0.8%
44a95676-39ce-4c49-9339-80e4809b2485 1
 
0.8%
f44783eb-4040-43c2-aac4-23f59b2e3507 1
 
0.8%
85dc4bbd-9867-435a-ab00-a1262d1717d6 1
 
0.8%
b7040293-d7fa-43cf-98f4-2fbd3c67d800 1
 
0.8%
363a23e7-0f8e-46fa-a267-1a652cb67cd0 1
 
0.8%
36cb3d8b-bcc6-4e42-bef3-c6de9e5e16f0 1
 
0.8%
ec0221cc-fa35-41fb-b8c5-97d9e3343103 1
 
0.8%
Other values (114) 114
91.9%

Most occurring characters

ValueCountFrequency (%)
- 496
 
11.1%
4 379
 
8.5%
9 273
 
6.1%
f 263
 
5.9%
b 263
 
5.9%
6 255
 
5.7%
8 250
 
5.6%
3 249
 
5.6%
2 239
 
5.4%
7 238
 
5.3%
Other values (7) 1559
34.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2530
56.7%
Lowercase Letter 1438
32.2%
Dash Punctuation 496
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 379
15.0%
9 273
10.8%
6 255
10.1%
8 250
9.9%
3 249
9.8%
2 239
9.4%
7 238
9.4%
0 230
9.1%
5 216
8.5%
1 201
7.9%
Lowercase Letter
ValueCountFrequency (%)
f 263
18.3%
b 263
18.3%
e 235
16.3%
d 233
16.2%
a 230
16.0%
c 214
14.9%
Dash Punctuation
ValueCountFrequency (%)
- 496
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3026
67.8%
Latin 1438
32.2%

Most frequent character per script

Common
ValueCountFrequency (%)
- 496
16.4%
4 379
12.5%
9 273
9.0%
6 255
8.4%
8 250
8.3%
3 249
8.2%
2 239
7.9%
7 238
7.9%
0 230
7.6%
5 216
7.1%
Latin
ValueCountFrequency (%)
f 263
18.3%
b 263
18.3%
e 235
16.3%
d 233
16.2%
a 230
16.0%
c 214
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4464
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 496
 
11.1%
4 379
 
8.5%
9 273
 
6.1%
f 263
 
5.9%
b 263
 
5.9%
6 255
 
5.7%
8 250
 
5.6%
3 249
 
5.6%
2 239
 
5.4%
7 238
 
5.3%
Other values (7) 1559
34.9%

shot_one_on_one
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)16.7%
Missing23055
Missing (%)> 99.9%
Memory size360.3 KiB
True
 
6
(Missing)
23055 
ValueCountFrequency (%)
True 6
 
< 0.1%
(Missing) 23055
> 99.9%
2023-02-07T18:52:42.889805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

shot_open_goal
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing23060
Missing (%)> 99.9%
Memory size360.3 KiB
True
 
1
(Missing)
23060 
ValueCountFrequency (%)
True 1
 
< 0.1%
(Missing) 23060
> 99.9%
2023-02-07T18:52:42.960820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

shot_outcome
Categorical

Distinct7
Distinct (%)4.0%
Missing22884
Missing (%)99.2%
Memory size360.3 KiB
Off T
53 
Saved
42 
Blocked
41 
Goal
28 
Wayward
8 
Other values (2)
 
5

Length

Max length16
Median length5
Mean length5.5028249
Min length4

Characters and Unicode

Total characters974
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlocked
2nd rowSaved
3rd rowBlocked
4th rowOff T
5th rowGoal

Common Values

ValueCountFrequency (%)
Off T 53
 
0.2%
Saved 42
 
0.2%
Blocked 41
 
0.2%
Goal 28
 
0.1%
Wayward 8
 
< 0.1%
Post 3
 
< 0.1%
Saved Off Target 2
 
< 0.1%
(Missing) 22884
99.2%

Length

2023-02-07T18:52:43.035838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-07T18:52:43.150853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
off 55
23.5%
t 53
22.6%
saved 44
18.8%
blocked 41
17.5%
goal 28
12.0%
wayward 8
 
3.4%
post 3
 
1.3%
target 2
 
0.9%

Most occurring characters

ValueCountFrequency (%)
f 110
11.3%
d 93
 
9.5%
a 90
 
9.2%
e 87
 
8.9%
o 72
 
7.4%
l 69
 
7.1%
57
 
5.9%
O 55
 
5.6%
T 55
 
5.6%
v 44
 
4.5%
Other values (13) 242
24.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 683
70.1%
Uppercase Letter 234
 
24.0%
Space Separator 57
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 110
16.1%
d 93
13.6%
a 90
13.2%
e 87
12.7%
o 72
10.5%
l 69
10.1%
v 44
 
6.4%
c 41
 
6.0%
k 41
 
6.0%
r 10
 
1.5%
Other values (5) 26
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
O 55
23.5%
T 55
23.5%
S 44
18.8%
B 41
17.5%
G 28
12.0%
W 8
 
3.4%
P 3
 
1.3%
Space Separator
ValueCountFrequency (%)
57
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 917
94.1%
Common 57
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 110
12.0%
d 93
10.1%
a 90
9.8%
e 87
9.5%
o 72
 
7.9%
l 69
 
7.5%
O 55
 
6.0%
T 55
 
6.0%
v 44
 
4.8%
S 44
 
4.8%
Other values (12) 198
21.6%
Common
ValueCountFrequency (%)
57
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 974
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 110
11.3%
d 93
 
9.5%
a 90
 
9.2%
e 87
 
8.9%
o 72
 
7.4%
l 69
 
7.1%
57
 
5.9%
O 55
 
5.6%
T 55
 
5.6%
v 44
 
4.5%
Other values (13) 242
24.8%

shot_statsbomb_xg
Real number (ℝ)

Distinct173
Distinct (%)97.7%
Missing22884
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean0.11628095
Minimum0.008663155
Maximum0.85191363
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.3 KiB
2023-02-07T18:52:43.276881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.008663155
5-th percentile0.013609394
Q10.027892498
median0.053639628
Q30.1300262
95-th percentile0.45118443
Maximum0.85191363
Range0.84325047
Interquartile range (IQR)0.1021337

Descriptive statistics

Standard deviation0.16025237
Coefficient of variation (CV)1.3781481
Kurtosis8.086593
Mean0.11628095
Median Absolute Deviation (MAD)0.03349559
Skewness2.7516634
Sum20.581727
Variance0.025680821
MonotonicityNot monotonic
2023-02-07T18:52:43.392908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.76 5
 
< 0.1%
0.027892498 1
 
< 0.1%
0.05906041 1
 
< 0.1%
0.10561356 1
 
< 0.1%
0.016010124 1
 
< 0.1%
0.04572172 1
 
< 0.1%
0.06480945 1
 
< 0.1%
0.050127037 1
 
< 0.1%
0.05537464 1
 
< 0.1%
0.035382725 1
 
< 0.1%
Other values (163) 163
 
0.7%
(Missing) 22884
99.2%
ValueCountFrequency (%)
0.008663155 1
< 0.1%
0.0098505495 1
< 0.1%
0.010779817 1
< 0.1%
0.011697948 1
< 0.1%
0.012766986 1
< 0.1%
0.012915427 1
< 0.1%
0.0130706495 1
< 0.1%
0.013131896 1
< 0.1%
0.013478487 1
< 0.1%
0.013642121 1
< 0.1%
ValueCountFrequency (%)
0.85191363 1
 
< 0.1%
0.76 5
< 0.1%
0.5152156 1
 
< 0.1%
0.46842504 1
 
< 0.1%
0.45880395 1
 
< 0.1%
0.44927955 1
 
< 0.1%
0.40083995 1
 
< 0.1%
0.35600668 1
 
< 0.1%
0.34840992 1
 
< 0.1%
0.3432793 1
 
< 0.1%

shot_technique
Categorical

IMBALANCE  MISSING 

Distinct5
Distinct (%)2.8%
Missing22884
Missing (%)99.2%
Memory size360.3 KiB
Normal
145 
Half Volley
22 
Volley
 
8
Diving Header
 
1
Overhead Kick
 
1

Length

Max length13
Median length6
Mean length6.700565
Min length6

Characters and Unicode

Total characters1186
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.1%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowHalf Volley
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal 145
 
0.6%
Half Volley 22
 
0.1%
Volley 8
 
< 0.1%
Diving Header 1
 
< 0.1%
Overhead Kick 1
 
< 0.1%
(Missing) 22884
99.2%

Length

2023-02-07T18:52:43.501932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-07T18:52:43.609957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
normal 145
72.1%
volley 30
 
14.9%
half 22
 
10.9%
diving 1
 
0.5%
header 1
 
0.5%
overhead 1
 
0.5%
kick 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
l 227
19.1%
o 175
14.8%
a 169
14.2%
r 147
12.4%
N 145
12.2%
m 145
12.2%
e 34
 
2.9%
V 30
 
2.5%
y 30
 
2.5%
24
 
2.0%
Other values (13) 60
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 961
81.0%
Uppercase Letter 201
 
16.9%
Space Separator 24
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 227
23.6%
o 175
18.2%
a 169
17.6%
r 147
15.3%
m 145
15.1%
e 34
 
3.5%
y 30
 
3.1%
f 22
 
2.3%
i 3
 
0.3%
v 2
 
0.2%
Other values (6) 7
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
N 145
72.1%
V 30
 
14.9%
H 23
 
11.4%
D 1
 
0.5%
O 1
 
0.5%
K 1
 
0.5%
Space Separator
ValueCountFrequency (%)
24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1162
98.0%
Common 24
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 227
19.5%
o 175
15.1%
a 169
14.5%
r 147
12.7%
N 145
12.5%
m 145
12.5%
e 34
 
2.9%
V 30
 
2.6%
y 30
 
2.6%
H 23
 
2.0%
Other values (12) 37
 
3.2%
Common
ValueCountFrequency (%)
24
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 227
19.1%
o 175
14.8%
a 169
14.2%
r 147
12.4%
N 145
12.2%
m 145
12.2%
e 34
 
2.9%
V 30
 
2.5%
y 30
 
2.5%
24
 
2.0%
Other values (13) 60
 
5.1%

shot_type
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)1.7%
Missing22884
Missing (%)99.2%
Memory size360.3 KiB
Open Play
170 
Penalty
 
5
Free Kick
 
2

Length

Max length9
Median length9
Mean length8.9435028
Min length7

Characters and Unicode

Total characters1583
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOpen Play
2nd rowOpen Play
3rd rowOpen Play
4th rowOpen Play
5th rowOpen Play

Common Values

ValueCountFrequency (%)
Open Play 170
 
0.7%
Penalty 5
 
< 0.1%
Free Kick 2
 
< 0.1%
(Missing) 22884
99.2%

Length

2023-02-07T18:52:43.712980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-07T18:52:43.820015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
open 170
48.7%
play 170
48.7%
penalty 5
 
1.4%
free 2
 
0.6%
kick 2
 
0.6%

Most occurring characters

ValueCountFrequency (%)
e 179
11.3%
n 175
11.1%
P 175
11.1%
l 175
11.1%
a 175
11.1%
y 175
11.1%
172
10.9%
O 170
10.7%
p 170
10.7%
t 5
 
0.3%
Other values (6) 12
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1062
67.1%
Uppercase Letter 349
 
22.0%
Space Separator 172
 
10.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 179
16.9%
n 175
16.5%
l 175
16.5%
a 175
16.5%
y 175
16.5%
p 170
16.0%
t 5
 
0.5%
r 2
 
0.2%
i 2
 
0.2%
c 2
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
P 175
50.1%
O 170
48.7%
F 2
 
0.6%
K 2
 
0.6%
Space Separator
ValueCountFrequency (%)
172
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1411
89.1%
Common 172
 
10.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 179
12.7%
n 175
12.4%
P 175
12.4%
l 175
12.4%
a 175
12.4%
y 175
12.4%
O 170
12.0%
p 170
12.0%
t 5
 
0.4%
F 2
 
0.1%
Other values (5) 10
 
0.7%
Common
ValueCountFrequency (%)
172
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1583
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 179
11.3%
n 175
11.1%
P 175
11.1%
l 175
11.1%
a 175
11.1%
y 175
11.1%
172
10.9%
O 170
10.7%
p 170
10.7%
t 5
 
0.3%
Other values (6) 12
 
0.8%

substitution_outcome
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)5.0%
Missing23021
Missing (%)99.8%
Memory size360.3 KiB
Tactical
38 
Injury
 
2

Length

Max length8
Median length8
Mean length7.9
Min length6

Characters and Unicode

Total characters316
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTactical
2nd rowTactical
3rd rowTactical
4th rowTactical
5th rowTactical

Common Values

ValueCountFrequency (%)
Tactical 38
 
0.2%
Injury 2
 
< 0.1%
(Missing) 23021
99.8%

Length

2023-02-07T18:52:43.911035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-07T18:52:44.013058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
tactical 38
95.0%
injury 2
 
5.0%

Most occurring characters

ValueCountFrequency (%)
a 76
24.1%
c 76
24.1%
T 38
12.0%
t 38
12.0%
i 38
12.0%
l 38
12.0%
I 2
 
0.6%
n 2
 
0.6%
j 2
 
0.6%
u 2
 
0.6%
Other values (2) 4
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 276
87.3%
Uppercase Letter 40
 
12.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 76
27.5%
c 76
27.5%
t 38
13.8%
i 38
13.8%
l 38
13.8%
n 2
 
0.7%
j 2
 
0.7%
u 2
 
0.7%
r 2
 
0.7%
y 2
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
T 38
95.0%
I 2
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 316
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 76
24.1%
c 76
24.1%
T 38
12.0%
t 38
12.0%
i 38
12.0%
l 38
12.0%
I 2
 
0.6%
n 2
 
0.6%
j 2
 
0.6%
u 2
 
0.6%
Other values (2) 4
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 316
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 76
24.1%
c 76
24.1%
T 38
12.0%
t 38
12.0%
i 38
12.0%
l 38
12.0%
I 2
 
0.6%
n 2
 
0.6%
j 2
 
0.6%
u 2
 
0.6%
Other values (2) 4
 
1.3%

substitution_replacement
Categorical

Distinct28
Distinct (%)70.0%
Missing23021
Missing (%)99.8%
Memory size360.3 KiB
Carli Anne Hollins
6 
Christen Annemarie Press
5 
Ali Krieger
 
2
Mallory Pugh
 
2
Lindsey Michelle Horan
 
2
Other values (23)
23 

Length

Max length36
Median length22
Mean length18.15
Min length10

Characters and Unicode

Total characters726
Distinct characters49
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)57.5%

Sample

1st rowPikul Khueanpet
2nd rowCarli Anne Hollins
3rd rowChristen Annemarie Press
4th rowTaneekarn Dangda
5th rowMallory Pugh

Common Values

ValueCountFrequency (%)
Carli Anne Hollins 6
 
< 0.1%
Christen Annemarie Press 5
 
< 0.1%
Ali Krieger 2
 
< 0.1%
Mallory Pugh 2
 
< 0.1%
Lindsey Michelle Horan 2
 
< 0.1%
Andrea Sánchez Falcón 1
 
< 0.1%
Nahikari García Pérez 1
 
< 0.1%
Georgia Stanway 1
 
< 0.1%
Jade Moore 1
 
< 0.1%
Samantha June Mewis 1
 
< 0.1%
Other values (18) 18
 
0.1%
(Missing) 23021
99.8%

Length

2023-02-07T18:52:44.100078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
carli 6
 
5.6%
hollins 6
 
5.6%
anne 6
 
5.6%
christen 5
 
4.7%
annemarie 5
 
4.7%
press 5
 
4.7%
lindsey 2
 
1.9%
horan 2
 
1.9%
michelle 2
 
1.9%
francesca 2
 
1.9%
Other values (62) 66
61.7%

Most occurring characters

ValueCountFrequency (%)
n 76
 
10.5%
e 76
 
10.5%
a 68
 
9.4%
67
 
9.2%
i 59
 
8.1%
r 51
 
7.0%
l 43
 
5.9%
s 36
 
5.0%
o 24
 
3.3%
A 18
 
2.5%
Other values (39) 208
28.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 553
76.2%
Uppercase Letter 106
 
14.6%
Space Separator 67
 
9.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 76
13.7%
e 76
13.7%
a 68
12.3%
i 59
10.7%
r 51
9.2%
l 43
7.8%
s 36
6.5%
o 24
 
4.3%
h 17
 
3.1%
t 15
 
2.7%
Other values (19) 88
15.9%
Uppercase Letter
ValueCountFrequency (%)
A 18
17.0%
C 14
13.2%
H 11
10.4%
P 10
9.4%
M 10
9.4%
S 7
 
6.6%
L 5
 
4.7%
J 4
 
3.8%
F 4
 
3.8%
D 4
 
3.8%
Other values (9) 19
17.9%
Space Separator
ValueCountFrequency (%)
67
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 659
90.8%
Common 67
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 76
 
11.5%
e 76
 
11.5%
a 68
 
10.3%
i 59
 
9.0%
r 51
 
7.7%
l 43
 
6.5%
s 36
 
5.5%
o 24
 
3.6%
A 18
 
2.7%
h 17
 
2.6%
Other values (38) 191
29.0%
Common
ValueCountFrequency (%)
67
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 718
98.9%
None 8
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 76
 
10.6%
e 76
 
10.6%
a 68
 
9.5%
67
 
9.3%
i 59
 
8.2%
r 51
 
7.1%
l 43
 
6.0%
s 36
 
5.0%
o 24
 
3.3%
A 18
 
2.5%
Other values (34) 200
27.9%
None
ValueCountFrequency (%)
é 2
25.0%
í 2
25.0%
ó 2
25.0%
ö 1
12.5%
á 1
12.5%

tactics
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing23035
Missing (%)99.9%
Memory size360.3 KiB

team
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size360.3 KiB
United States Women's
12610 
France Women's
1803 
England Women's
1741 
Sweden Women's
1536 
Spain Women's
1470 
Other values (3)
3901 

Length

Max length21
Median length21
Mean length18.198821
Min length13

Characters and Unicode

Total characters419683
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnited States Women's
2nd rowThailand Women's
3rd rowUnited States Women's
4th rowThailand Women's
5th rowUnited States Women's

Common Values

ValueCountFrequency (%)
United States Women's 12610
54.7%
France Women's 1803
 
7.8%
England Women's 1741
 
7.5%
Sweden Women's 1536
 
6.7%
Spain Women's 1470
 
6.4%
Netherlands Women's 1463
 
6.3%
Chile Women's 1301
 
5.6%
Thailand Women's 1137
 
4.9%

Length

2023-02-07T18:52:44.201101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-07T18:52:44.313126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
women's 23061
39.3%
united 12610
21.5%
states 12610
21.5%
france 1803
 
3.1%
england 1741
 
3.0%
sweden 1536
 
2.6%
spain 1470
 
2.5%
netherlands 1463
 
2.5%
chile 1301
 
2.2%
thailand 1137
 
1.9%

Most occurring characters

ValueCountFrequency (%)
e 57383
13.7%
n 46562
11.1%
t 39293
9.4%
s 37134
8.8%
35671
 
8.5%
' 23061
 
5.5%
W 23061
 
5.5%
o 23061
 
5.5%
m 23061
 
5.5%
a 21361
 
5.1%
Other values (16) 90035
21.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 302219
72.0%
Uppercase Letter 58732
 
14.0%
Space Separator 35671
 
8.5%
Other Punctuation 23061
 
5.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 57383
19.0%
n 46562
15.4%
t 39293
13.0%
s 37134
12.3%
o 23061
7.6%
m 23061
7.6%
a 21361
 
7.1%
d 18487
 
6.1%
i 16518
 
5.5%
l 5642
 
1.9%
Other values (6) 13717
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
W 23061
39.3%
S 15616
26.6%
U 12610
21.5%
F 1803
 
3.1%
E 1741
 
3.0%
N 1463
 
2.5%
C 1301
 
2.2%
T 1137
 
1.9%
Space Separator
ValueCountFrequency (%)
35671
100.0%
Other Punctuation
ValueCountFrequency (%)
' 23061
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 360951
86.0%
Common 58732
 
14.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 57383
15.9%
n 46562
12.9%
t 39293
10.9%
s 37134
10.3%
W 23061
6.4%
o 23061
6.4%
m 23061
6.4%
a 21361
 
5.9%
d 18487
 
5.1%
i 16518
 
4.6%
Other values (14) 55030
15.2%
Common
ValueCountFrequency (%)
35671
60.7%
' 23061
39.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419683
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 57383
13.7%
n 46562
11.1%
t 39293
9.4%
s 37134
8.8%
35671
 
8.5%
' 23061
 
5.5%
W 23061
 
5.5%
o 23061
 
5.5%
m 23061
 
5.5%
a 21361
 
5.1%
Other values (16) 90035
21.5%

timestamp
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct15709
Distinct (%)68.1%
Missing0
Missing (%)0.0%
Memory size360.3 KiB
00:00:00.000
 
47
00:15:57.091
 
6
00:35:10.407
 
4
00:01:43.213
 
4
00:14:10.063
 
4
Other values (15704)
22996 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters276732
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9139 ?
Unique (%)39.6%

Sample

1st row00:00:00.000
2nd row00:00:00.000
3rd row00:00:00.000
4th row00:00:00.000
5th row00:00:00.000

Common Values

ValueCountFrequency (%)
00:00:00.000 47
 
0.2%
00:15:57.091 6
 
< 0.1%
00:35:10.407 4
 
< 0.1%
00:01:43.213 4
 
< 0.1%
00:14:10.063 4
 
< 0.1%
00:16:14.227 4
 
< 0.1%
00:30:36.158 4
 
< 0.1%
00:05:29.981 4
 
< 0.1%
00:38:20.856 4
 
< 0.1%
00:16:23.344 4
 
< 0.1%
Other values (15699) 22976
99.6%

Length

2023-02-07T18:52:44.415149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00.000 47
 
0.2%
00:15:57.091 6
 
< 0.1%
00:30:36.158 4
 
< 0.1%
00:16:23.344 4
 
< 0.1%
00:38:20.856 4
 
< 0.1%
00:05:29.981 4
 
< 0.1%
00:22:25.599 4
 
< 0.1%
00:16:14.227 4
 
< 0.1%
00:14:10.063 4
 
< 0.1%
00:01:43.213 4
 
< 0.1%
Other values (15699) 22976
99.6%

Most occurring characters

ValueCountFrequency (%)
0 66979
24.2%
: 46122
16.7%
. 23061
 
8.3%
2 20601
 
7.4%
1 20242
 
7.3%
3 19766
 
7.1%
4 19236
 
7.0%
5 15621
 
5.6%
7 11428
 
4.1%
8 11362
 
4.1%
Other values (2) 22314
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 207549
75.0%
Other Punctuation 69183
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 66979
32.3%
2 20601
 
9.9%
1 20242
 
9.8%
3 19766
 
9.5%
4 19236
 
9.3%
5 15621
 
7.5%
7 11428
 
5.5%
8 11362
 
5.5%
6 11251
 
5.4%
9 11063
 
5.3%
Other Punctuation
ValueCountFrequency (%)
: 46122
66.7%
. 23061
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 276732
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 66979
24.2%
: 46122
16.7%
. 23061
 
8.3%
2 20601
 
7.4%
1 20242
 
7.3%
3 19766
 
7.1%
4 19236
 
7.0%
5 15621
 
5.6%
7 11428
 
4.1%
8 11362
 
4.1%
Other values (2) 22314
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 276732
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 66979
24.2%
: 46122
16.7%
. 23061
 
8.3%
2 20601
 
7.4%
1 20242
 
7.3%
3 19766
 
7.1%
4 19236
 
7.0%
5 15621
 
5.6%
7 11428
 
4.1%
8 11362
 
4.1%
Other values (2) 22314
 
8.1%

type
Categorical

Distinct32
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size360.3 KiB
Pass
6233 
Ball Receipt*
5722 
Carry
5116 
Pressure
2113 
Ball Recovery
824 
Other values (27)
3053 

Length

Max length17
Median length16
Mean length7.7237327
Min length4

Characters and Unicode

Total characters178117
Distinct characters47
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowStarting XI
2nd rowStarting XI
3rd rowHalf Start
4th rowHalf Start
5th rowHalf Start

Common Values

ValueCountFrequency (%)
Pass 6233
27.0%
Ball Receipt* 5722
24.8%
Carry 5116
22.2%
Pressure 2113
 
9.2%
Ball Recovery 824
 
3.6%
Duel 568
 
2.5%
Clearance 376
 
1.6%
Dribble 302
 
1.3%
Block 232
 
1.0%
Goal Keeper 217
 
0.9%
Other values (22) 1358
 
5.9%

Length

2023-02-07T18:52:44.511175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ball 6546
21.5%
pass 6233
20.5%
receipt 5722
18.8%
carry 5116
16.8%
pressure 2113
 
6.9%
recovery 824
 
2.7%
duel 568
 
1.9%
clearance 376
 
1.2%
dribble 302
 
1.0%
foul 279
 
0.9%
Other values (33) 2350
 
7.7%

Most occurring characters

ValueCountFrequency (%)
e 20675
11.6%
a 19229
 
10.8%
s 18122
 
10.2%
r 16799
 
9.4%
l 15551
 
8.7%
P 8546
 
4.8%
c 7497
 
4.2%
7368
 
4.1%
t 7083
 
4.0%
i 7001
 
3.9%
Other values (37) 50246
28.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 134522
75.5%
Uppercase Letter 30444
 
17.1%
Space Separator 7368
 
4.1%
Other Punctuation 5732
 
3.2%
Decimal Number 40
 
< 0.1%
Dash Punctuation 11
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 20675
15.4%
a 19229
14.3%
s 18122
13.5%
r 16799
12.5%
l 15551
11.6%
c 7497
 
5.6%
t 7083
 
5.3%
i 7001
 
5.2%
p 6339
 
4.7%
y 5981
 
4.4%
Other values (13) 10245
7.6%
Uppercase Letter
ValueCountFrequency (%)
P 8546
28.1%
B 6789
22.3%
R 6557
21.5%
C 5637
18.5%
D 1271
 
4.2%
S 303
 
1.0%
F 280
 
0.9%
G 219
 
0.7%
K 217
 
0.7%
M 190
 
0.6%
Other values (8) 435
 
1.4%
Other Punctuation
ValueCountFrequency (%)
* 5722
99.8%
/ 10
 
0.2%
Decimal Number
ValueCountFrequency (%)
5 20
50.0%
0 20
50.0%
Space Separator
ValueCountFrequency (%)
7368
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 164966
92.6%
Common 13151
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 20675
12.5%
a 19229
11.7%
s 18122
11.0%
r 16799
10.2%
l 15551
9.4%
P 8546
 
5.2%
c 7497
 
4.5%
t 7083
 
4.3%
i 7001
 
4.2%
B 6789
 
4.1%
Other values (31) 37674
22.8%
Common
ValueCountFrequency (%)
7368
56.0%
* 5722
43.5%
5 20
 
0.2%
0 20
 
0.2%
- 11
 
0.1%
/ 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 178117
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 20675
11.6%
a 19229
 
10.8%
s 18122
 
10.2%
r 16799
 
9.4%
l 15551
 
8.7%
P 8546
 
4.8%
c 7497
 
4.2%
7368
 
4.1%
t 7083
 
4.0%
i 7001
 
3.9%
Other values (37) 50246
28.2%

under_pressure
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing17861
Missing (%)77.5%
Memory size360.3 KiB
True
5200 
(Missing)
17861 
ValueCountFrequency (%)
True 5200
 
22.5%
(Missing) 17861
77.5%
2023-02-07T18:52:44.607192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

goalkeeper_lost_in_play
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing23060
Missing (%)> 99.9%
Memory size360.3 KiB
True
 
1
(Missing)
23060 
ValueCountFrequency (%)
True 1
 
< 0.1%
(Missing) 23060
> 99.9%
2023-02-07T18:52:44.677208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

goalkeeper_punched_out
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing23060
Missing (%)> 99.9%
Memory size360.3 KiB
True
 
1
(Missing)
23060 
ValueCountFrequency (%)
True 1
 
< 0.1%
(Missing) 23060
> 99.9%
2023-02-07T18:52:44.746224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

foul_committed_offensive
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)11.1%
Missing23052
Missing (%)> 99.9%
Memory size360.3 KiB
True
 
9
(Missing)
23052 
ValueCountFrequency (%)
True 9
 
< 0.1%
(Missing) 23052
> 99.9%
2023-02-07T18:52:44.824242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

foul_committed_penalty
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)20.0%
Missing23056
Missing (%)> 99.9%
Memory size360.3 KiB
True
 
5
(Missing)
23056 
ValueCountFrequency (%)
True 5
 
< 0.1%
(Missing) 23056
> 99.9%
2023-02-07T18:52:45.082300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

foul_won_penalty
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)20.0%
Missing23056
Missing (%)> 99.9%
Memory size360.3 KiB
True
 
5
(Missing)
23056 
ValueCountFrequency (%)
True 5
 
< 0.1%
(Missing) 23056
> 99.9%
2023-02-07T18:52:45.150315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

miscontrol_aerial_won
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)11.1%
Missing23052
Missing (%)> 99.9%
Memory size360.3 KiB
True
 
9
(Missing)
23052 
ValueCountFrequency (%)
True 9
 
< 0.1%
(Missing) 23052
> 99.9%
2023-02-07T18:52:45.226332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_miscommunication
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)33.3%
Missing23058
Missing (%)> 99.9%
Memory size360.3 KiB
True
 
3
(Missing)
23058 
ValueCountFrequency (%)
True 3
 
< 0.1%
(Missing) 23058
> 99.9%
2023-02-07T18:52:45.304350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_no_touch
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing23059
Missing (%)> 99.9%
Memory size360.3 KiB
True
 
2
(Missing)
23059 
ValueCountFrequency (%)
True 2
 
< 0.1%
(Missing) 23059
> 99.9%
2023-02-07T18:52:45.381367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

injury_stoppage_in_chain
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)16.7%
Missing23055
Missing (%)> 99.9%
Memory size360.3 KiB
True
 
6
(Missing)
23055 
ValueCountFrequency (%)
True 6
 
< 0.1%
(Missing) 23055
> 99.9%
2023-02-07T18:52:45.456384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

clearance_other
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)33.3%
Missing23058
Missing (%)> 99.9%
Memory size360.3 KiB
True
 
3
(Missing)
23058 
ValueCountFrequency (%)
True 3
 
< 0.1%
(Missing) 23058
> 99.9%
2023-02-07T18:52:45.529401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

goalkeeper_shot_saved_off_target
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing23059
Missing (%)> 99.9%
Memory size360.3 KiB
True
 
2
(Missing)
23059 
ValueCountFrequency (%)
True 2
 
< 0.1%
(Missing) 23059
> 99.9%
2023-02-07T18:52:45.610419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

shot_saved_off_target
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing23059
Missing (%)> 99.9%
Memory size360.3 KiB
True
 
2
(Missing)
23059 
ValueCountFrequency (%)
True 2
 
< 0.1%
(Missing) 23059
> 99.9%
2023-02-07T18:52:45.699429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_deflected
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing23059
Missing (%)> 99.9%
Memory size360.3 KiB
True
 
2
(Missing)
23059 
ValueCountFrequency (%)
True 2
 
< 0.1%
(Missing) 23059
> 99.9%
2023-02-07T18:52:45.783447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Interactions

2023-02-07T18:52:24.853554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:12.767593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:13.915853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:15.128127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:16.423429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:17.548673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:18.687930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:19.804182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:21.138483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:22.295746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:23.518021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:24.933571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:12.870616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:14.031878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:15.236151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:16.529442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:17.663700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:18.795954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:19.906205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:21.242507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:22.407770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:23.627045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:25.025592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:12.986642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:14.155907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:15.345175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:16.642467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:17.773724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:18.902979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:20.015229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:21.354532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:22.527797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:23.742071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:25.106611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:13.091666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:14.265931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:15.445198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:16.744493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:17.879761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:19image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:20.126254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:21.461557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:22.639822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:23.847095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:25.185628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:13.193690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:14.370955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:15.546220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:16.843513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:17.982779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:19.106025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:20.416334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:21.563579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:22.744847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:23.953120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:25.263646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:13.301715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:14.477980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:15.652245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:16.946537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:18.100797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:19.214049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:20.519344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:21.664602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:22.852871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:24.056142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:25.340663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:13.414739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:14.588004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:15.755267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:17.051560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:18.201820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:19.314072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:20.628369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:21.765625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:22.970898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:24.161166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:25.420682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:13.516762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:14.693027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:15.857290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:17.151582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:18.300842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:19.413094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:20.730391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:21.870649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:23.085923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:24.266189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:25.499700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:13.615784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:14.805053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:15.957313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:17.252606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:18.397864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:19.510115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:20.831414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:21.979674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:23.191948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:24.546495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:25.589721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:13.725810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:14.926089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:16.073339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:17.366632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:18.506889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:19.621141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:20.944440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:22.097700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:23.309973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:24.660659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:25.679740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:13.824831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:15.034104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:16.172362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:17.468655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:18.609912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:19.722164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:21.048463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:22.205725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:23.422000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-07T18:52:24.768535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Missing values

2023-02-07T18:52:26.256881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-07T18:52:27.516169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-02-07T18:52:31.185161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

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